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Biomedical Imaging - Science topic

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Basically I was Interested in Skin Diseases Detection Using Image Processing
Kindly suggest me technology to be used and a research problem
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I suggest you use deep neural network models for disease diagnosis.
This field is very interesting.
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Suppose I have a dicom file with pixel size (0.5,0.5), and I want to make the pixel size to (0.7,0.7) or vice versa. How can I change/modify the pixel size of a dicom file?
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I'm just stuck for days in this very step, my question is how to save the result after this line of code as dcm or any medical image format?
image = scipy.ndimage.interpolation.zoom(image, real_resize_factor, mode='nearest')
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1. The necessity of a polarization controller for single-mode fiber. Is a polarization controller necessary for single-mode fibers? What happens when you don't have a polarization controller?
2. Optical path matching problem. How to ensure that the two arms of the optical path difference match, any tips in the adjustment process? If the optical path difference exceeds the imaging distance, will interference fringes fail to appear?
Only these questions for the time being, if there are more welcome to point out.
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1. The polarization can change during propagation. This will degrade the visibility of the fringes. You need a polarization controller or a polarization-maintaining fiber.
2. By the imaging distance, do you mean the distance to the sample or the sample's dimension? In any case, if the OPD exceeds the coherence lenght of your source, the interference fringes disappear. In order to match the OPD, we typically sweep the reference arm a long distance and record the output intensity. Another method is to monitor the output spectrum using a spectrometer. The spectrum shows oscillations for non-zero OPD. When the OPD approaches zero, the spectrum oscillations tend to disappear.
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Dear researchers.
I have recently started my research in detecting and tracking brain tumors with the help of artificial intelligence, which includes image processing.
What part of this research is valuable, and what do you suggest for the most recent part that is still useful for a PhD. research proposal?
Thank you for participating in this discussion.
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In current technology era, to sustain and provide healthy life to humans it is necessary to detect the diseases in early stages. We are focused on Brain tumour detection process, it is very challenging task in medical image processing. Through early diagnosis of brain, we can improve treatment possibilities and increase the survival rate of the patients. Recently, deep learning plays a major role in computer vision, using deep learning techniques to reduction of human judgements in the process of diagnosis. Proposed model is efficient than traditional model and provides best accuracy values. The experimental results are clearly showing that, the proposed model outperforms in the detection of brain tumour images.
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Could you tell me please what is the effect of electromagnetic waves on a human cell? And how to model the effect of electromagnetic waves on a human cell using image processing methods?
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Dear Sir
I also recommend the reference suggusted above by Dr. Guynn because it provides a wide detail about these kinds of issues. For the modeling, I suggest to try the FDTD (finite difference time domain) metheod since it can model any medium by patitioning it into small cells (similar to pixels).
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Hi all,
I am looking for experts in area of Biomedical Image Processing.
Any recommendations ?
Please share
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Hi
Are DBN (deep belief networks)-based approaches promising for Biomedical Image segmentation? or they are overcome by CNN ?
Do you have any items to recommend ? Are there any source codes available on Github?
Thanks R.
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While they can be used, CNNs have more alternatives to explore and has a history backing the same. So particularly for the task of medical image segmentation it would be a better choice to use CNNs. As Mahmoud Khaled Abd-Ellah has mentioned U-Nets, there are extensions to that as well which make it the better model of the 2.
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I wonder if anybody knows where I could find and download some ultrasound images? I need them for performing the despeckling algorithm.
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want to know recent ongoing research related to biomedical image processing
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How to get the Image number info in DICOM files? I tried 'dicominfo' but it doesn't get the 'image number' info in the DICOM header.
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With Matlab use this instruction:
Dicominfo
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I would like to know if any carbon allotrope based nanomaterials (such as fullerenes, nanotubes, graphene, reduced graphene, graphene oxide, nanohorns, nanoonions, graphene quantum dots, carbon dots or carbon nanoparticles) have ever been used for imaging/tracking of viruses/ viral protein/ viral genome in vivo/ in vitro. I have read articles where they have been used in conjunction with viruses for imaging tumours or for treatment of diseases with drugs but I am not looking for them. I have been scouring for research works in this area but all I could find were metallic nanoparticles, semiconductor quantum dots and very rarly some polymeric/dendritic materials. However, I could find no literature on carbon allotrope based nanomaterials for virus imaging.
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Manuel Gómez Thanks a lot for the articles. I am working on a literature survey actually and I am particularly looking for any research from the past 5 years that have used carbon allotropic nanomaterials for imaging viruses. Since 2016, I have found only 1-2 nanodiamond based virus imaging studies. I know that all these allotropes generally have fluorescent properties and they have been used for bioimaging a host of different targets. However, what I couldn't find is any recent progress on such materials within the past 5 yerars particularly for viruses. For example, I have tried looking for single virus tracking using these nanomaterials and the search always comes empty. By tracking, I am talking about in vivo-in vitro imaging of different things such as cells/ zebrafish embryos. mice using microscopic imaging methods such as bright field microscopy, etc. Although the papers you have refernced in your response are helpful to gain a better understanding of the nanomaterial's characteristics in the context of fluorescence, I could unfortunately not succeed in finding any particular research pertaining to viruses or justifying the absence of it
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I need a dermoscopic image database to test an algorithm for automatic diagnose. In particular I am interested in images with blue-black colors within the lesion.
Can anyone tell me where to find it?
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We invite you to apply for the PhD position with the topic "Automatic identification of structures in biomedical mega-images" using the following link: https://tinyurl.com/rqh6qf9
It will be in collaboration between myself and Prof. Ben Giepmans (UMCG) and will have the opportunity to work on large biomedical images in nanotomy generated by a state-of-the-art electron microscope (EM).
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Ph.D. scholarship is not found. I would glad that inform me if there is a similar position.
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Dear colleagues,
We are pleased to announce the 9th International Workshop on Biomedical Image Registration, WBIR2020, hosted in Portorož, Slovenia! The workshop will be held in the Congress Centre Bernardin in Portorož, on 16 and 17 June, 2020.
The workshop brings together leading researchers in the area of biomedical image registration to present and discuss recent developments in the field, including methodological innovations and advances in the performance and validation on existing and novel applications. The workshop will include both oral and poster presentations, exciting keynote lectures, all with ample opportunities for discussion. At the social events you will enjoy the warm and relaxing Adriatic seaside along with authentic Mediterranean cuisine and excellent drinks.
IMPORTANT DATES Paper submission deadline: Jan 10, 2020 Notification of acceptance: Feb 21, 2020 Camera-ready deadline: March 20, 2020 Conference dates: June 16 and 17, 2020
AIMS AND SCOPE Submissions are invited in all areas of biomedical image registration. Topics of interest include, but are not limited to:
- Novel registration methodology: 2D/3D/4D, spatiotemporal/dynamic, pairwise / groupwise, slice-to-volume, projective, single/multi-modal, intra/inter-subject, model-based, patch-based, multi-channel, tracking
- Mathematical aspects of image registration: continuous/discrete optimization, real- time, similarity measures, diffeomorphisms, LDDMM, stationary velocity, inverse consistency, multi-scale
- Machine learning and deep learning techniques for registration: unsupervised / supervised / reinforcement learning, convolutional / recurrent / transformer networks, neural networks for feature extraction and matching, correspondence weighting and prediction, attention modeling, deformation learning, deep encoder- decoder networks
- Biomedical applications of registration: computer-assisted interventions, image- guided therapy, treatment planning/delivery, diagnosis/prognosis, atlas-based segmentation, label fusion, histopathology correlation, serial studies, pathology detection and localization, morphometry, biomechanics, image retrieval/restoration/fusion, imaging biomarkers for precision medicine, radiomics & radiogenomics, early proofs of concept
- Validation of registration: quantitative and qualitative methods, benchmarking, comparison studies, phantom studies, correlation to outcome, validation protocols and performance metrics, uncertainty estimation
All accepted full paper submissions will be published as a volume in the Springer's Lecture Notes in Computer Science (LNCS) series.
ORGANIZING COMMITTEE Ziga Spiclin, University of Ljubljana, Ljubljana, Slovenia Jamie McClelland, University College London, London, UK Jan Kybic, Czech Technical University in Prague, Prague, Czech Republic Orcun Goksel, ETH Zurich, Zurich, Switzerland
SPONSORS The WBIR 2020 is a MICCAI Society Endorsed Event (www.miccai.org).
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Thank you very much for sharing this announcement ... It seems interesting, a recent look at the site where it was announced about trying to find an alternative date to hold the conference, due to the Corona pandemic. I hope that you will inform us of any updates in the event of setting another date .. I hope goodness and safety For everyone...
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Can anyone suggest a dataset that publicly available MRI images (JPEG,TIFF ) dataset with tumors?
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Hi there,
I hope you are doing well.
I am working with imaging systems. I am confused about the effects of linear polarizer in such systems ( I mean how a linear polarizer can improve the resolution?) and why working with one polarization is better than two polarization in image processing systems?
Bests,
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Dear William,
Thank you for your response.
Actually the light source that we simulate is a point source light that has spectral width of 50 nm with peak around 400 nm and the detector is photo multiplier tube.
Bests
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I am looking for free online database of Brain Hemorrhage CT images for my research work. 
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Please find below a link a dataset of intracranial hemorrhage segmentation.
I know my reply is too late, but just in case, other research are looking for similar datasets.
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Scope
Biomedical imaging has emerged as a major technological platform for disease diagnosis, clinical research, drug development and other related domains due to being non-invasive and producing multi-dimensional data. An abundance of imaging data is collected, but this wealth of information has not been utilized to full extent. Therefore, there is a need for introduction of biomedical image analysis techniques that are accurate, reproducible and generalizable to large scale datasets. Identification of imaging patterns in an anatomical site or an organ at the macroscopic or microscopic scale may guide characterization of abnormalities and estimation of disease risk, analysis of large scale clinical datasets, and assessment of intervention therapy techniques.
This special track solicits original and good-quality papers for delineating, identifying and characterizing novel biomedical imaging patterns. These methods may aim for segmentation, identification and quantification of anatomies, characterization of their properties, and classification of disease. These approaches may be applied to radiological imaging such as CT, MRI and ultrasound, or imaging at the cellular scale such as microscopy and digital pathology techniques.
Topics
Topics of interest include, but are not limited, to the following areas:
Machine/Deep Learning for Computer-aided Diagnosis and Prognosis
Biomedical Image Segmentation and Registration
Radiomics, Immunotherapies, Digital Pathology
Cell Segmentation and Tracking
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It's a great topic.
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Histological analysis.
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The edge detection takes a wide interest in the field of digital image processing and computer vision, which is one of the basic stages in the process of patterns recognition and image segmentation. In Digital image processing, a set of methods and algorithms are applied on data of digital images in order to obtain some basic information that can be used in a particular application such as image enhancement, image segmentation , edge detection and others. The image processing algorithms have been updated as a result of the development in computer technologies such as processing speed and ease of computer acquisition in addition to the development of cameras. Digital images consist a set of elements (pixels) formed as a two-dimensional matrix the edge is a narrow area produced between two different regions in the values of brightness or difference between object and background. Edges detection methods have most important in the processing of digital images, which is a key stage in the distinction of patterns. Most edge detection methods high pass filter pass the image and mathematical convolution between filter and image.
Papers:
Tiwaskar 2013 "Comparison Of Edge Detection Techniques", Proceedings Of Sixth International Conference 2015.
Mingfeng Zhu, Jianqiang Du, and ChenghuaDing, 2014: "A Comparative Study of Contemporary Color Tongue Image Extraction Methods Based on HSI", International Journal of Biomedical Imaging.
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i m working in the field you may find the various methods but when it comes to human perception, true positive true negative ,false positive ,false negative are most reliable merits. They might be more time consuming but way more accurate.
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I don't know what to do anymore. Everything I use is fresh, daily prepared. All solutions were remade, but I always get this effect. In the image is alpha-tubulin. 
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I had this same effect some times and after changing all the buffers I discovered that it was because the acrylamide bottle had been in use for long time. Since then, I have a small aliquot approx 25-30 ml for use. Make sure that the acrylamide bottle has not been open for a long time, and as Dr. Vaidyanathan says, let the gel polymerize slowly overnight at 4 C, this also improves the resolution of the bands. .
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Dear All,
I am interested in using machine learning to analyze MRI data in order
to extract pathological changes for neurodegenerative diseases (i.e Alzheimer's, Huntington's )
Can someone please give some advice on what (bioinformatic,statistical) methods from your experience would be best for an initial analysis and to extract disease specific features ?
Also what would be the most useful platform and what visualization tools or popular packages are best for data extraction and presentation in this case ?
I have about 500 samples from Philips and Siemens scanners ... Could you also suggest the processing power that I would typically require ... for example If I want to train my data and create module or signature prediction algorithms and what MRI parameters would be most informative in each case ( i.e FLAIR, DTI ?)
Any help would be greatly appreciated
Regards
Anastasios
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Clinical analyzes are a branch of science responsible for performing laboratory tests using biological materials such as urine, feces, and blood. The results of these analyzes are responsible for directly impacting 70% of the medical decisions, because through them it is possible to identify etiological agents and indicate the best treatment. The most requested laboratory examination is the hemogram. This methodology is responsible for qualitatively and quantitatively analyzing blood cells. This test is subdivided into 3 parts called erythrogram, leukogram and platelet, which analyze erythrocytes, leukocytes and platelets respectively
Papers:
Green R. and Wachsmann-Hogiu S. “Development, History, and Future of Automated Cell Counters”. Clinics in laboratory medicine. 35. 1-10. 10.1016/j.cll.2014.11.003., 2015.
Hoofman R.; et al. “Hematology: Basic principles and practice”. 6th es. Canadá: Elsevier, 2013.
Reyes-Aldosoro C.C. “Biomedical Image Analysis Recipes in Matlab: For Life and Engineers”. London: Wiley Blackweel, 2015.
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I would suggest to run a Google Scholar search/Patent search with "
WTH-MO Algorithms" Segmentation Hemograms and IPC marks for A61B5 and G06T and subclassifcations. You may want to use ESP@CENET and runs a search with simialr keywords. A review article that covers the state-of-the art would be a good start.
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Hello, I am currently researching a spectral computed tomography system for medical applications. The system excels in delineating the elemental composition of materials, providing a 3D representation of the each element's distribution. This has shown excellent results in determining the composition of calcifications but I'm wondering what other diseases researchers are aware of that might benefit from this technique.
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The elements that would be involved in examples I mentioned would copper, Iron and the compound silica.
Do you think this method could work in some of these cases?
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I am just a beginner in cell culture. I opt HeLa cells as the first.
As of now, I know, to culture any cells, we need media, antibiotics and many other reagents. However, depending on each cell type above mentioned reagent condition changes. For HeLa cells, what is the best reagent conditions is acceptable? I have found tons of document to learn from. Eventually, some are conflicting with each other. For example, some says DMEM (High Glucose, L-Glutamine)+10%FBS is suitable, Some says DMEM (High Glucose+Pyruvate) is suitable, and some says EMEM is suitable.
I would highly highly appreciate all your inputs, comes from your research experience and expertise.
N.B.: I will need HeLa cells for not to perform any core biological study but for Biomedical Imaging purpose.
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Thanks, Olivier Dubey , and Vikrant Mehta for making things clear and easy to deal with. I appreciate your help.
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Photo-acoustic biomedical instrumentation will be a trend in the future. It uses very high sound frequency (in 100's of GHz) way above ultrasound range (1MHz to 18MHz). If there is anyone who is familiar with the technology, who is working with such a project, please let me know and give some of your ideas here.
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Sheryl Roberts , M. Manivannan , Subhamoy Mandal thank you foe your comment. Sheryl Roberts , thank you, I appreciate your suggestion, I will do that.
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Hi,
I want to use a MRI images dataset in order to detect heart failure with image processing techniques. In the beginning I should choose the kind of map I need. T1 map or T2 map, I should choose one of them.
But I don’t know what is the differences between them and which one is better for detecting heart failure.
Can anyone share some information about that?
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T1 mapping allows you to quantify extracellular volume (ECV). Normal values for ECV are 25-28%, higher ECV values most usually represent interstitial myocardial fibrosis (as in hypertension, hypertrophic cardiomyopathy, severe aortic stenosis,...), values over 40% represent amyloidosis or infarction-related scar
T2 mapping measures specifically interstitial edema (thus, it is usually employed to detect the acute phase of myocardial infarction or acute myocarditis, as overtime edema will disappear)
One important point, though. Heart failure is defined CLINICALLY not by MRI techniques. MRI can help you to identify the cause of HF, but HF itself is diagnosed clinically (clinical history, physical examination, chest X ray,...)
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Hi, I would like to create a weight map from a binary image in a way to have more intensity values in the border pixels between the close objects. To make it more clear I need to calculate w(x) from an input image following the formula mentioned below. Where w0 and sigma are two constant value, x denotes pixels in the image, d1 denotes the distance to the border of the nearest object and d2 is distance to the border of the second nearest cell.
This is what is done in U-net paper ( )but I do not how to implement this in python or Matlab
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Hello,
To calculate w(x) you need to find all the instances in the ground truth and then compute the distance transform for each individual object. With this distance map, the equation implementation is straightforward as d1(x) and d2(x) are the smallest values in the distance map for pixel x. Please find attached a Matlab implementation of the original equation (including class balance weights w_c) and the result over the input image.
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DTI images of brain tumors with ground truth. It is noted that there is no DTI available in the BRATS dataset.
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Dear Guevara,
Thank you for your reply, unfortunately there is no MRI multi modal images with DTI with tumors ground truth in NITRC Image Repository .
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I would appreciate it if someone explains the basic physical principles underlying the differences in appearance (image content, and tissue differentiation) among T1, T2, and PD weighted images.
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This is a broad question that would require a detailed introduction to MRI for a full answer.
In short, different tissues are characterized by different proton densities (PD) and different relaxation time constants (T1 for the longitudinal relaxation, T2 for the transverse relaxation).
Depending on the MRI acquisition technique (pulse sequence) and the sequence parameters (such as repetition time TR, echo time TE, or inversion time TI), the resulting image can reflect one of the tissue parameters much stronger than the others. E.g., a T2-weighted image (with very long TR and TE in the range of T2) emphasizes differences in T2 (showing tissues with long T2 such as fluids much brighter than those with shorter T2). In a T1-weighted image (with very short TE and TR in the range of T1), differences in T2 are hardly visible, but tissues with long T1 appear darker than those with shorter T1.
For a conventional spin-echo sequence, this is described by the signal equation:
S(TE, TR; T1, T2) = S0 (1 – exp(–TR/T1)) exp(–TE/T2)
See also my answer to the following related question:
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Sometimes the one who's injured may not be be able to talk and score his pain. for several medical actions the intensity of pain and it's position is required. I've been told using fMRI somehow helps, yet I'm wondering how and I would like to know if there's any more accurate device to measure pain intensity.
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Pain is a very delicate matter, in general, it is a neurological transmission which is interpretation is "pain". One can use electro-measurement devices detect the intensity of the transmission of the "pain". However, it will not tell you "how much it hurts" for different people can both tolerate different levels of pain, and the feeling of pain may vary by subjecting the people to different chemicals (natural or artificial).
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Hello all,
I am a researcher working on color image processing and have knowledge in Machine Learning and Programming. Currently, I am planning to deviate my research towards the Medical and Biomedical application. Suggest some research topics in Medical/Biomedical application which has scope in future?
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Biomedical sciences are applied sciences applying portions of natural science to interventions, or technology that are of use in healthcare or public health such as medical microbiology, clinical virology, clinical epidemiology, genetic epidemiology, and biomedical engineering are medical sciences. I researched in .
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The image of the main plane for ex. coronal plane always are of the highest quality, although others( sagital and axial in this case) have worse,so machine somehow converts the data.
Where I can find information about the process of creating auxiliary planes? Can anyone help me?
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Hi Damian,
each slice is characterised by the in-plane resolution, for example in your images that you acquired in the coronal plane the in-plane resolution is determined by FOV^2/Phase encoding x frequency encoding. If you have an 160 mm FOV and 256x320 matrix then the in-plane resolution is 180/256 x 180/320 = 0.625 x 0.5 mm. Remember however that each slice has a slice thicknes. In 2D MRI routinely in the musculoskeletal system we use 3 mm thicknes. Which means that if you process your data in the sagittal plane the resolution of the sagittal image will be 0.625 mm (or 0.5 mm if the frequency-encoding gradient of the original image is in the sagittal plane) and 3 mm in the other direction. Resolution 0.625 x 3 mm. So you understand that in plane resolution in the sagittal plane is poor in one direction. However, if you had aquired 3D slices with a slice thicknes of 1 mm or less the images would then be in all planes. Plain mathematics my friend.
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I'm trying to analyse images of animal tissues produced with a 3DHistech slide scanner. I usually use ImageJ but have problems fitting large enough images in the memory. Are there any other free alternatives, or tricks that could be used with ImageJ? My analysis is simple, just detecting & measuring circular holes of certain size (adipocytes). The images are like 25000 pixels x 25000 pixels.
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QuPath Open source software for digital pathology
Orbit Image Analysis
Both software are easier than matlab. There cut images into tiles automatically.
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Distance between object and sensor is 2mm.
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Hey Kamrul, 
I would agree with Indrasen's assessment. I would also suggest to think in terms of spatial frequncies. If your detector is placed in the optical far-field, you will directly measure the Fourier transform. The numercial aperture of your detector will then limit the spatial frequency extent of your Fourier transform, i.e. your detector acts as a low pass filter. If you are measuring with a lensless setup in the optical near-field, you can just as much think in terms of the spatial frequency spectrum by using the angular spectrum formalism (ASPW); the ASPW will lead to the same reasoning as Indrasan's argument above. 
However, in most lensless microscopes you will have to do some sort of phase retrieval that allows you to refocus the wave field into the specimen plane. The ability to successfully do phase retrieval typically depends on some sort of data redundancy (as in ptychography) or a priori knowledge (as in single shot coherent diffraction imaging or holography [where the reference wave is assumed to be known]). Departure from idealized assumptions (a priori knowledge), mathematical models, systematic experimental errors and noise will lead to a decrease in resolution. For this reason, the so called Fourier ring correlation (FRC*) is used in electron microscopy and x-ray ptychography to assess the reproducibilty of computationally retrieved spatial frequency spectra. Let's say you you are computationally reconstructing a microscopic sample with two different randomized initial guesses (assuming some sort of iterative reconstruction algorithm). Then the two different initial guesses will yield two slightly different reconstructions. From my experience in this field, typically the low frequencies are well reconstructed, the high frequencies are less reproducible. But only reproducible spatial frequencies are resolved spatial frequencies. Therefore I would recommend using the FRC for resolution estimation.
Finally, it is always worth taking a resolution test target (such as spokes targets). While those are typically binary test objects  and biological specimen are harder to reconstruct, they give you at least an idea of what the resolution of your microscopes is. 
Having said that, take also a look at this ** reference. Be also aware to always distinguish between half pitch and full pitch resolution (***). Simply stated, if you are able to resolve a harmonic grating (sinusoid), then due to the Shannon-Nyquist sampling criterion it takes two samples per spatial period to digitally represent this signal. Then the half period is the half pitch, the full period is the full pitch. 
Best wishes,
Lars
* Van Heel, M., & Schatz, M. (2005). Fourier shell correlation threshold criteria. Journal of structural biology, 151(3), 250-262.
** Horstmeyer, R., Heintzmann, R., Popescu, G., Waller, L., & Yang, C. (2016). Standardizing the resolution claims for coherent microscopy. Nature Photonics, 10(2), 68.
*** Zheng, G., Horstmeyer, R., & Yang, C. (2015). Corrigendum: Wide-field, high-resolution Fourier ptychographic microscopy. Nature Photonics, 9(9), 621.
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hi everyone,
I try to quantify the biodistribution via pet/ct images but I have some problems, for example I am not sure that is that ok if I apply any filter such as 3D Gaussian on SPECT or PET images? 
I am working with p-mod  software.
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There is always a resolution/noise tradeoff when using filtering. If you use larger kernels, you will reduce more noise but small structures will loose contrast. If you are trying to quantify organ level distributions, then filtering is ok and won´t change your results. On the other hand, if you are quantifying small lesions, your measurements will get lower as you increase the filter size due to partial volume effects. 
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I would like to purchase a system that allows me to collect ultrasound video images of skeletal-muscle at high sampling rates (e.g., >100 Hz) during muscle contractions. Does anyone have any advice on the best systems out there for this in terms of their image quality and ease of use? ALso how much might I expect to pay? Thanks.
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Toshiba Aplio-20 is good
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Hello, i'm working on a research about source localization involved in face processing.  I need to do a statistical comparison between two conditions: face perception and scrambled face perception; what software can i use to represent these statistical maps on the subjects MRI?
thanks
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Not that I'm aware of. As Dr. Schulreich above suggested, SPM also does this. If you are comparing maps from different subjects, you first need to warp them into a common space, which SPM also does really nicely. I've just found it hard to use SPM for EEG data, compared to EEGLAB and LORETA, but if you are not happy about a standard template, SPM might be the best way to go.  
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Hi all,
I segmented the whole femur of a patient (including the tibia portion) and extracted its 3-D model, but it is not hollow, while I segmented the femur of that patient without tibia portion and extracted its 3-D model, it is hollow. I used the same method for theses two models and minimum and maximum threshold values are 226 and 3071 respectively for these two models.
I want to have the hollow 3-D model of femur including tibia portion. Would you please help me to remove this problem?
Best regards,
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3D Slicer (www.slicer.org) has great manual and automatic segmentation tools. It's free, open-source, available on all platforms. Download the latest nightly version (not the latest stable, as there have been many improvements since the last stable) and use the Segment Editor module. See for example this tutorial to get started: https://www.slicer.org/wiki/Documentation/Nightly/Training#Segmentation_for_3D_printing. Feel free to write to the slicer-users mailing list if you have any questions.
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I would like to get sample tomosynthesis imaging
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yes i want this type of image
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I am analyzing 3D-reconstructed images taken of embryo vasculature. In these images, I am trying to understand the behavior of endothelial cells; comparing controls with mutants. So far I managed to have the X,Y, Z coordinates of these cells. I am trying to figure out how clumpy they are. And compare that between controls and mutants. 
So basically I am trying to analyze the data using these coordinates. I am sure that there some sort of formula or plot that can show me how clumped these cells are. 
I am wondering if anyone has a good background with this.
Thank you, 
Ali 
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Given the centres of the cells you could look at the cumulative distribution of the distances between all pairs of cells.
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Thins vessels in retinal image have low frequency. So I need to enhanced vessels before proccessing. matched filtering enhanced vessel but that can't extract thin vessels very well. I need a better filter than MF. which transform is better? curvelet? can any body explain curvelet and send the code?
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Thin vessels in retinal image indicates high frequency. You can try with curvelet and Gabor transform as it has more orientation than wavelet transform.
Thanks & Regards,
Sumaiya.
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Hi everyone,
I am aiming at assessing fat fraction (FF) in the liver relying on Chemical Shift Imaging (MRI). What is the most reliable and widely available MRI sequence (as an improved version of the two point Dixon)? According to this sequence, did the inter-machine reproducibility of Fat Fraction assessment known?
Thank you in advance for your feedback
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Dear Hubert,
while MRS is considered most accurate but restricted to a small sampling volume in the liver, CSI enables you to assess FF and its distribution across the whole liver with sufficient accuracy. As a starting source, I highly recommend this overview paper from Reeder et al. [1].
Typically, a multi-echo gradient recalled echo (GRE) sequence is used for fat quantification. Note that the classical two point Dixon is simply a special case of this. For water-fat separation, two echoes (in- and opposed-phase) are basically enough to seperate the spectral components of water and fat. Yet, there a couple of confounding factors such as R2* = 1/T2*, T1 bias or phase effects that need to be addressed [1,2,3]. Typical studies then end up using a 6-echo protocol (to fit for R2* decay), a low flip angle of 2 to 4 degree (to minimize T1 weighting) and optimized echo times.
The choice of echo times can be crucial since it controls the noise behaviour and, depending on your fitting routine (magnitude- or complex-based), can affect accuracy apart from robustness [3]. As a rule of thumb, you want to use a very short first echo (TE1) as this yields high SNR and at the same time a delta TE such that you capture the field-strength dependent resonance between the water and fat components well-enough (e.g., dTE ~1.2 ms at 3T). On current hardware, you can easily acquire a 1.7 to 1.2 mm^2 in-plane resolution with a slice thickness of 4 to 5 mm within a single breathhold (dependent on #slices and parallel imaging acceleration). For current and examplary clinical protocols see here [4,5].
I hope this helps to set a starting point. Let me know if you need more detail.
Best regards.
[1] Reeder, Scott B., et al. "Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy." Journal of magnetic resonance imaging 34.4 (2011): 729-749.
[2] Yu, Huanzhou, et al. "Multiecho water‐fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling." Magnetic resonance in medicine 60.5 (2008): 1122-1134.
[3] Liu, Chia Ying, et al. "Fat quantification with IDEAL gradient echo imaging: correction of bias from T1 and noise." Magnetic resonance in medicine 58.2 (2007): 354-364.
[4] Artz, Nathan S., et al. "Reproducibility of MR‐based liver fat quantification across field strength: Same‐day comparison between 1.5 T and 3T in obese subjects." Journal of Magnetic Resonance Imaging 42.3 (2015): 811-817.
[5] Hernando, Diego, et al. "Multisite, multivendor validation of the accuracy and reproducibility of proton density fat fraction quantification at 1.5 T and 3T using a fat–water phantom." Magnetic resonance in medicine (2016).
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i used matlab toolbox which use intensity based registration,
the image that i want to register to the reference image doesn't have good intensity, and makes algorithm not to work properly.
here is my two images
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Any chance the original used a mechanical cross hair marker? If not, try stretching the image in x or y i f you had variance on grazing angle. You can LPF after that.
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I'm doing an imaging study in patients with sciatic nerve radiculopathy, and I'm trying to find out the vertebral level that is adjacent to where the L4, L5, and S1 nerve roots enter the cord.
I understand that there will likely be a large amount of variability among people, and most of the resources I've seen so far suggest that they enter the cord somewhere between the level of L1, T12, and T11 vertebrae.
Does anyone know of any studies (e.g. fiber tracing, DWI) that have attempted to more formally answer this question? 
Thanks for your help!
Dan
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HI Dirk, thanks for your response. I am interested in tracking the roots - L4 to S1. However, my original question should have been more clear: I'm actually interested in finding out where the roots terminate in the cord. Because I'm interested in sensory afferents carrying nociceptive information, and because these fibers enter into Lissauer's tract, traveling up or down several segments before synapsing, I wondered if there were any good references describing where they are most likely to terminate. 
If anyone is interested, a colleague directed me to the following publication, which may have some insights into the question:
Thanks,
Dan
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Does anybody know about / have experience with flourometers? I need to track where a fluorescent dye goes over time in a flow field.  It should be measured at multiple locations in the flow field.  
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Are you trying to image your entire flow field?  If so, you'll want to be a bit careful with your choice of filters: many filters sold for fluorescence are based on thin film technology which is highly angle dependent.  The field of view of a typical camera lens could allow quite a bit of excitation light leakage, complicating your signal.
r/
David
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Hi,
Does anyone know the source/database from where I can download CT images (same patient and same anatomy) which are acquired with different kVp levels? I need to analyze the histogram patterns of HU from such images.
- Manjunath
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I have to classify few objects based on the boundary discontinuities. Are there any specific geometrical shape descriptors available? And how good is HOG for identifying shapes?
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Check "SHAPE CONTEXT" and "INNER DISTANCE SHAPE CONTEXT". Forget HoG and rest of methods ;).
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Multiple sclerosis(MS) is demyelinating disease in which the insulating covers of nerve cells in the brain and spinal cord are damaged . In some cases we use FES ( functional electrical stimulation) to cure lack of walking in leg .But Is there any solution in order to improve the conductivity of neuron in absence of insulating covers and after that use a system like FES in other cases for example Visionary system ?
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 Dear Behrad,
It is a good question. Unfortunately we currently have no cure for MS and also don't know how to tackle the demyelination of the axons. FES (functional electrical stimulation) can only provide limited functional recovery but it is not the cure. Without the myelin the axons are short-circuited and therefore without fixing the demyelination by remyelination, it is not possible to cure by FES. Furthermore FES has its own limitations as a method, such as specific stimulation of individual nerves or coordinated stimulation of multiple nerves at will.
Best wishes, Refik
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I am not professional about compressed sensing (CS). I know CS can reconstruct the texture of the original image from highly undersampled data if some conditions are met. However, I have no clue how CS algorithms do to the noise. Is it possible that compressed sensing reconstructed image in MRI maintains or improves SNR comparing to fully sampled original image?
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A noise reduction (or SNR improvement) due to the CS reconstruction is certainly possible - however, typically associated with loss of resolution or image details.
Compressed sensing reconstruction can be implemented using a total variation (TV) penalty in addition to an l1-norm sparsity objective. The TV penalty performs additional (edge-preserving) denoising of the resulting image - look e.g. for "TV" or "denoising" in the article reference below. (The strength/extent of denoising is controlled by the weighting factor (alpha) of the TV penalty.)
(TV denoising can also be applied to other MR images - independent of CS reconstruction; this will also improve the SNR, but potentially remove small image details.)
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I have isolated recombinant plasmid using all buffer solutions from GeneJet Mini prep plasmid isolation kit except resuspension solution. I used resuspension solution of promega Midi prep kit. I got band during electrophoresis. Will this change of one solution from another company affect downstream steps of my experiment?
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Hi,
tipically commercial solutions for resuspending DNA are almost quite similar between each other. They are essentially slightly alkaline solutions (pH 8) with or without a cation chelator (tipically EDTA). The effects on downstream steps are difficoult to predict without knowing witch experiments you are running. I have to advise you that a buffer containing EDTA could reduce the yield of subsequent enzymatic reactions that often need metal cations. After DNA isolation using commercial kits, I usually resuspend DNA pellet in sterile water to avoid this problem and always worked well.
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I am imaging some optic nerve head sections using non linear microscopy (SHG/TPEF simultanously). I am going to merge the two channels (SGH green and TPEF red) in order to see the co-localisation of collagen and elastin. I am getting some autofluorescence from TPEF coming from the whole sample so it makes difficult to distinghuish elastin from everything else.
What can I do to avoi/remove/reduce auto fluorescence?
Thank you
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Hi,
let me explain how I prepared the samples. As I am in the first year of my PhD I had some optic nerves kept in PFA that have been collected few years ago. Anyway, I removed the sample from the PFA and I cut 250 microns thick longitudinal sections with a sledge microtome and I put them in a cling film, stored at -80 until imaging.
Then I thaw the samples and once they have reached room temperature I put them in a petri dish with some PBS: glycerol 1:1. I used an immersion lens as we are trying to develop a new method for non linear microscopy using immersion lens. But the auto fluorescence has been seen with a normal lens too (maybe it was less intense), using a positive control for elastin. 
Is this what you were are asking for?
Please let me know.
Kind regards
Laura
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Which classification tool is better for Medical Image analysis. I am planning on working on medical images to facilitate image works within help sector.
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For medical image analysis you can use Wndchrm.
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[cA,cD] = dwt(sig, 'db1'); i have used this code to decompose a signal and i have obtained CA and CD of Size (Ixn) but if i change the code to some other 'wname' like db2, db4 etc i am getting a signal of size of (I x(n-1)) but i require a size of (IXn). kindly help me how to solve this
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Before you do the DWT decomposition, you can add zero at the end of your signal.
sig = [sig 0]
[cA,cD] = dwt(sig, 'db2');
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I want to do research about retinal images and finally extract vessel and optic disk and exudates. but i cant find angiogram database.
who can help me?
and
you think which one is better for my Thesis? segmentation from fondus images(DRIVE) or angiography images?
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Dear Mrs.Shaabani
there are some appropriate database as your need:
moreover below dataset site can be helpful:
bellow survey reference can be useful for second Question about data selection:
[1] Fraz, Muhammad Moazam, et al. "Blood vessel segmentation methodologies in retinal images–a survey." Computer methods and programs in biomedicine 108.1 (2012): 407-433.
:
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Hi everyone,
I am interested in a small sample of X ray enema DICOM. I mostly want to test display of some software and to roughly evaluate image quality.
Thank you for your help
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I have a database of anonymised images that should be of assistance.  How many do you require?  Please email me at fmii@outlook.com.au; I look forward to assisting.
Kind regards
Rob
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Clustering of a & b is being done by k-means clustering
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Instead of k-means, you can try to segment in a simpler (but reliable) way using the Lab Joint Histrogram. It takes very little to be implemented and its results could fit your problem. For an explanation have a look to:  
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Are there any metrics to compare the robustness of 2 features for an image? I need to compare and choose which feature vector would be apt for my spine image dataset.
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i found out that HOG features are best when compared with eigen features or fisher features. i don't know particularly about the spine images in specific, but i can tell you that you can start with finding the HOG features. 
i worked on face images. you can see my paper explaining about finding the HOG features and the importance of HOG features. 
instead of spending time in identifying the methodology for the best feature vector, you can work with already existing and experimentally proven features one by one. i hope you will be succeeded with the first experiment.
MATLAB is the best platform for finding the features for images. 
plenty of MATLAB programs are available in the internet.
previously i tried other features like SIFT and so on. i didnot get as much output as HOG features. 
all the best
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i am trying to start research in medical image processing, which type of medical image is in recent research and which one is to better?
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Brain parts and tissues, mammograms, lungs and biometric access. 
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I need the database of DRI images which can be used for identification of cancer. please help
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Hello,
First of all, Which kind of cancer are you trying to study?, when I was searching about glioblastoma multiforme brain's cancer (GBM) I found the  "Allen Brain Atlas". In this site you could find biopsy images. I've got some works about fluorescence and reflectance in skin's cancer but these are in spanish.
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How far these images can be used for identifying cancer?
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Dear Sreevidya
i think this paper is useful:
Phys Med Biol. 2006 Aug 7;51(15):3733-46. Epub 2006 Jul 12.
Quantification of bioluminescence images of point source objects using diffusion theory models.
Comsa DC1, Farrell TJ, Patterson MS.
Author information
1Juravinski Cancer Centre and McMaster University, 699 Concession Street, Hamilton, Ontario L8V 5C2, Canada.
Abstract
A simple approach for estimating the location and power of a bioluminescent point source inside tissue is reported. The strategy consists of using a diffuse reflectance image at the emission wavelength to determine the optical properties of the tissue. Following this, bioluminescence images are modelled using a single point source and the optical properties from the reflectance image, and the depth and power are iteratively adjusted to find the best agreement with the experimental image. The forward models for light propagation are based on the diffusion approximation, with appropriate boundary conditions. The method was tested using Monte Carlo simulations, Intralipid tissue-simulating phantoms and ex vivo chicken muscle. Monte Carlo data showed that depth could be recovered within 6% for depth 4-12 mm, and the corresponding relative source power within 12%. In Intralipid, the depth could be estimated within 8% for depth 4-12 mm, and the relative source power, within 20%. For ex vivo tissue samples, source depths of 4.5 and 10 mm and their relative powers were correctly identified.
good luck
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Hello Friends:
I'm sending out a general call for your help in deciding on a thesis topic for my MS degree. I had an MSEE and had worked as an electronics engineer for 20 years (in US Defense, FiberOptics, Data Storage, Machine Vision industries). Now decided to leave it behind and returned to school this past January 2016 for an MSBE degree (Ph.D. later on, God willing) since I wanted to meld my knowledge in electronics with the new field of bioengineering(new to me anyways). The problem is I have no life sciences background (Biology, Chemistry, BioChem, etc.) to speak of since high school (and that was a really long time ago!). All thesis project ideas I have seen are so biology/life science-based which I, unfortunately, can't do (without spending another year or two acquiring that knowledge). Designing standard bioelectronics devices such as heart rate monitors/pacemakers etc. bores me silly and honestly don't think one can submit that as a thesis topic as still be an honest individual. I have the following interests:
- Image Processing ... I designed an IP board as my MSEE thesis back in the day). Analyzing MRI scan images seem to have been done to death. Is it still a topic worthy of an MS degree? If so, any new challenges here?
- Bioimpedance measurements ... I'm new to this but find the whole concept intriguing and fascinating. But designing body fat measurement devices is also too boring and technically dumb. Not worth an MS degree in my opinion. Sorry!
- Biosensors ... new to them as a concept and find them also very intriguing and fascinating. But I'm afraid my lack of biology/biochemistry would be a hindrance and burden to me.
Any suggested project must be realistically doable within a 4-5 month timeframe, tops. I'd start in Spring 2017 then finish in the following summer.
Thanks All :)
- David
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A rapidly evolving area of very diverse  needs may be described broadly as “Independence Engineering”: Solutions for people living with disabilities, chronic disease or who are simply frail and aging, can incorporate significant innovation or adaptation of our existing technologies. It may not seem as sexy as many of the historical advances that leap to mind when thinking of Biomedical Engineering, but the marketplace is huge and growing, and many of the innovations require less life science knowledge. Understanding of neurotransmission, the cardiac cycle or the workings of the immune system are hardly vital to be effective .  Just an idea, Good Luck!....GW
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Hello,
I have ECG text files with two columns for the time span and the corresponding ECG value.
I am want to convert the text files into MIT format, so I can use them with WFDB software which detects the peaks and generate the RRI data.
I tried to use: ahaecg2mit code but I am got a lot of errors and the file was not converted.
How to convert the text format ECG data to MIT format in WFDB software?
Regards,
Asiya
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Dear Florian,
I have downloaded the WFDB MATLAB package and the mat2wfdb.m function did work with me. 
I did check the conversion by using (rdsamp) function to convert it back to text file again and plotting it against the original signal before conversion.
Thank you that was helpful.
Regards, 
Asiya
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Hi, I used the weighted AIC to calculate time of the first arrival ultrasound wave for  a simple phantom, the reconstructed images satisfied our needs, but for a complex phantom, in which there are many lesion tissues, the reconstructed images have a very low resolution, many lesion tissues link together, not separated. So i doubt the accuracy of the time of flight by weighted AIC. would you like to give me some advice? thank you
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AIC is the Akaike Information Criterion as you said
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Has anyone previously used K-nearest neighbour search method to verify co-registration of MRI Images? Greatly appreciate any insight into how this method can be applied for exactly that purpose.
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when you employ clustering with inhomogeneity correction the number of misregistrations is reduced without loss of accuracy thus increasing robustness as compared to the standard non-inhomogeneity corrected and equidistant binning based registration.
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Hello everyone,
in some biomechanical papers, the center of volume of a body segment is assumed to be equal to the center of mass. Is anyone aware of studies that provide statistical data on the three-dimensional spatial difference between the center of volume and mass for different body segments in female and male subjects?
So far, I could find some information in a technical report [1] (page 68f) using cadavers. They measured the percentage of body segment volume proximal to the center of mass. The positional difference is estimated to be "two to three centimeters" proximal.
Thank you in advance for your input.
[1] Clauser C, McConville J, Young J; Weight, volume and center of mass of segments of the human body; AMRL Technical Report; 1969
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Hello Marcelo,
thank you very much for this paper. Actually, I also found it two days ago ;) It is the best paper on this topic I have found so far.
Lephart et al. compare the results of a uniform and a variable density model for two male cadavers with different somatotypes. Unfortunately, they don't seem to give the exact difference between the COM (variable density) and COV (uniform density) for the individual segments in the result tables. But in the discussion, they write that the difference is quite small ( <1mm average, 2mm maximum).
Best regards.
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White patch with clear border is indication of cervical cancer. Whiteness range vary from image to image. 
Thanx in advance
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then go for adaptive or hybrid segmentation techniques , and yes morphological operations are best for manual ROI detection may be you are not using them up to there maximum potential
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Has anyone previously worked with ASL scans on the human retina? We are currently working on Siemens Skyra 3T and would appreciate any insights on the scan parameters that would be ideal.
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Dear Safal Khanal Sir,ASL MRI has been validated against other quantitative technologies such as Dynamic Susceptibility-Weighted Contrast-Enhanced MRI, PET scan, and using different exogenous contrast agents. Furthermore, since ASL image contrast is not based on susceptibility effects, it could be used to study regions of high static field inhomogeneity. This property of ASL is especially valuable while imaging highly layered structures such as the eye’s retina.ASL implementations are now commercially available on all major MRI platforms and their reproducibility has been confirmed by several multicentre studies. However, before applying this technique in the clinic, many of its technical parameters have to be studied and optimized. Here we are going to review the effects of a number of these factors on the quality of the ASL measurements.
Also you should note that The retinal circulation has no autonomic innervation and is regulated by local factors while the choroidal circulation is not autoregulated and is mainly controlled by sympathetic innervation. Using a 9 T Phillips magnet in a preliminary study, we have previously shown that ASL can be applied to image this nonregulated blood flow in the retina. 
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What would be the best toolbox/method/software which is helpful in analysing perfusion data obtained from ASL-MRI scan of the human retina?? Any insight is much appreciated!
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Hi,
This toolbox could be useful: https://cfn.upenn.edu/~zewang/ASLtbx.php
Cheers!
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As mentioned in the title, does anybody know how to draw biological tissue figure? is there any free access software? Thanks!
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Very useful! Thanks, Man!
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I am currently looking for one that can be used for simulations, and thus a non-computer graphics based algorithm/model to depict the pathway of light in multi-layered skin and tissue targets. 
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If you want to generlize your simulation to any geometry you can check up Nirfast an open source software
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I have the compound. I want to measure if it is also a photosensitizer using cell-free spectroscopy. Any ideas what molecular probes could be used?
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Dear Sir. Regarding your issue about how to measure photosensitizer properties of a compound. The following below links may help you in your analysis:
Thanks
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I have got area using regionprops, which is in pixel form. And I wish to calculate into mm2 (square of mm). Or if I can know the size of pixel by which I can convert area?   
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pixel size is determine from  sensor and based on that you have to get area however you required to know the vertical distance or where you have taken the image.
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Hello everyone, i have sample of oral skin of human effected from cancer. Due to no background of the biology, i am unable to get info from the image. I shall be very thankful to him/her( bio-medical Optics person, Medical Imaging person) if he/she tell me all possible details which can be extracted from that image. I have other images too but i am displaying here only one image which is got by 50X objective of Polarization microscopy. This image is polarization intensity.Other images, i can discuss privately.Thanks
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I will work with it today in our new and very powerful software package MIPAR (http://MIPAR.us). I am very confident I can extreme many parameters from your features. I'll make a recipe file for the automated analysis and I invite you to download MIPAR and run the recipe!
Just to clarify, I assume you are after a particle size distribution, and perhaps spacing and area fraction. Is this accurate?
Cheers,
John
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