Questions related to Biomedical Imaging
Basically I was Interested in Skin Diseases Detection Using Image Processing
Kindly suggest me technology to be used and a research problem
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?
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.
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.
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?
I wonder if anybody knows where I could find and download some ultrasound images? I need them for performing the despeckling algorithm.
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.
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).
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).
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?
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 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
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.
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.
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
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
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.
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.
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.
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.
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?
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 (
DTI images of brain tumors with ground truth. It is noted that there is no DTI available in the BRATS dataset.
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.
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.
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?
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?
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.
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.
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.
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?
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?
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.
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?
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
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!
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.
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?
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 ?
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?
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?
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?
Which classification tool is better for Medical Image analysis. I am planning on working on medical images to facilitate image works within help sector.
[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
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?
you think which one is better for my Thesis? segmentation from fondus images(DRIVE) or angiography images?
Clustering of a & b is being done by k-means clustering
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?
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 :)
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?
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
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.
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  (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.
 Clauser C, McConville J, Young J; Weight, volume and center of mass of segments of the human body; AMRL Technical Report; 1969
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.
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!
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.
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?
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