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In medical images, since there are body region and border that it is hard for medical worker to distinguish by the only image diagnosis, we estimate that the progress of work, time, and emergency are needed. Therefore, it is problem of emergency to develop the medical information system enable to support medical workers by using high performance computer. In this study, first, we carried out texture analysis for region of laparoscopic image. Next, based on results, we experimented whether region segmentation of laparoscopic image is possible or impossible. Finally, we discussed how each texture features are affected to region segmentation.
To read the full-text of this research, you can request a copy directly from the authors.
... In this paper, for color laparoscopic frame image cut from surgical video under laparoscopy, we carried out processing contrast enhancement using appropriate parameter obtained our previous study. 1,2,4,5 And then, in the case of processing SRCNN by image regions, we discussed comparing among PSNR, SSIM, and texture feature for contrast whether we are able to estimate and divide image regions or not. ...
As one of image pre-processing method to detect, recognize, and estimate lesion or characteristic region in medical image processing, there are many studies improved performance and precision of processing by contrast enhancement or super-resolution.
However, it is not clarified how condition is better to apply these methods. Therefore, we experimented and discussed on affect for color laparoscopic image quality by the difference of contrast enhancement method.
As a result, we obtained knowledge of high similarity among patterns of adaptive histogram equalization in three methods.
However, under these conditions, in the case of considering the region segmentation, it is not clarified how processing precision is better.
In this paper, first we processed the contrast enhancement for the color laparoscopic frame image cut from surgery video under laparoscopy.
Next, we processed super-resolution for generated image.
Finally, we compared and discussed by Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM), and texture features for contrast.
... In this paper, for color laparoscopic frame image cut from surgical video under laparoscopy, we carried out processing contrast enhancement using appropriate parameter obtained our previous study - . And then, in the case of processing SRCNN by image regions, we discussed comparing among PSNR, SSIM, and texture feature for contrast whether we are able to estimate and divide image regions or not. ...
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.
Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. This paper presents a literature review of basic image segmentation techniques from last five years. Recent research in each of image segmentation technique is presented in this paper.
A novel hybrid region-based active contour model is presented to segment medical images with intensity inhomogeneity. The energy functional for the proposed model consists of three weighted terms: global term, local term, and regularization term. The total energy is incorporated into a level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Experiments on some synthetic and real images demonstrate that our model is more efficient compared with the localizing region-based active contours (LRBAC) method, proposed by Lankton, and more robust compared with the Chan-Vese (C-V) active contour model.
A computer software system is designed for segmentation and classification of benign and malignant tumour slices in brain computed tomography images. In this study, the authors present a method to select both dominant run length and co-occurrence texture features of wavelet approximation tumour region of each slice to be segmented by a support vector machine (SVM). Two-dimensional discrete wavelet decomposition is performed on the tumour image to remove the noise. The images considered for this study belong to 208 tumour slices. Seventeen features are extracted and six features are selected using Student's t-test. This study constructed the SVM and probabilistic neural network (PNN) classifiers with the selected features. The classification accuracy of both classifiers are evaluated using the k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and the segmented tumour is presented in terms of segmentation accuracy and segmentation error. The proposed system provides some newly found texture features have an important contribution in classifying tumour slices efficiently and accurately. The experimental results show that the proposed SVM classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.
Up to now, the diagnostic imaging was carried out based on manual handling by professional doctors and health care workers. However, it is enable to diagnose images automatically by development of the computer systems. Therefore, it is required for approach from information science and engineering fields. For generating multi-view 3D images, if we are able to support whether workers are able to use correctly or not, and if the coded defect detection and restoration for their images are possible, we consider possibility towards medical applications in the near future. In this paper, first, we diagnosed automatically for multi-view 3D images in the case of occurring defect by encoded and decoded degradation at all or certain viewpoints by H.265/HEVC. Next, we assessed and estimated quantitatively in terms of the coded image quality in order to clarify how we are able to detect the coded defect.
Capsule Endoscopy (CE) is the first-line diagnostic tool for inspecting gastrointestinal (GI) tract diseases. It is a tremendous task on examining and managing the CE videos by endoscopists. Therefore, a computer-aided diagnosis system is desired and urgent. In this paper, a general cascaded spatial-temporal deep framework is proposed to understand the most commonly seen contents of whole GI tract videos. First, the noisy contents such as feces, bile, bubble, and low power images are detected and removed by a Convolutional Neural Network (CNN) model. The clear images are then classified into entrance, stomach, small intestine, and colon by the second CNN. Finally, the topographic segmentation of the whole video is performed with a global temporal integration strategy by Hidden Markov Model (HMM). Compared to existing methods, the proposed framework perform noise content detection and topographic segmentation at the same time, which significantly reduces the number of images to be checked by endoscopists and segment images of different organs more accurately. Experiments on a dataset with 630 K images from 14 patients demonstrate that the proposed approach achieves a promising performance in terms of effectiveness and efficiency.
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at
Knowledge of the specific anatomical information of a patient is important when planning and undertaking laparoscopic surgery due to the restricted field of view and lack of tactile feedback compared to open surgery. To assist this type of surgery, we have developed a surgical navigation system that presents the patient's anatomical information synchronized with the laparoscope position. This paper presents the surgical navigation system and its clinical application to laparoscopic gastrectomy for gastric cancer.
The proposed surgical navigation system generates virtual laparoscopic views corresponding to the laparoscope position recorded with a three-dimensional (3D) positional tracker. The virtual laparoscopic views are generated from preoperative CT images. A point-based registration aligns coordinate systems between the patient's anatomy and image coordinates. The proposed navigation system is able to display the virtual laparoscopic views using the registration result during surgery.
We performed surgical navigation during laparoscopic gastrectomy in 23 cases. The navigation system was able to present the virtual laparoscopic views in synchronization with the laparoscopic position. The fiducial registration error was calculated in all 23 cases, and the average was 14.0 mm (range 6.1-29.8).
The proposed surgical navigation system can provide CT-derived patient anatomy aligned to the laparoscopic view in real time during surgery. This system enables accurate identification of vascular anatomy as a guide to vessel clamping prior to total or partial gastrectomy.
In this paper, we develop a computer aided diagnosis algorithm to detect and classify the abnormalities in vision-based endoscopic examination. We focus on analyzing the traditional gastroscope data and help the medical experts improve the accuracy of medical diagnosis with our analysis tool. To achieve this, we first segment the image into superpixels, then extract various color and texture features from them and combine the features into one feature vector to represent the images. This approach is more flexible and accurate than the traditional patch-based image representation. Then we design a novel feature selection model with group sparsity, Deep Sparse SVM (DSSVM) that not only can assign a suitable weight to the feature dimensions like the other traditional feature selection models, but also directly exclude useless features from the feature pool. Thus, our DSSVM model can maintain the accuracy while reducing the computation complexity. Moreover, the image quality is also pre-assessed. For the experiments, we build a new gastroscope dataset with a total of about 3800 images from 1284 volunteers, and conducted various experiments and comparisons with other algorithms to justify the effectiveness and efficiency of our algorithm.
This paper presents an in-depth study of several approaches to exploratory analysis of wireless capsule endoscopy images (WCE). It is demonstrated that versatile texture and color based descriptors of image regions corresponding to various anomalies of the gastrointestinal tract allows their accurate detection of pathologies in a sequence of WCE frames. Moreover, through classification of single pixels described by texture features of their neighborhood, the images can be segmented into homogeneous areas well matched to the image content. For both, detection and segmentation tasks the same procedure is applied which consists of features calculation, relevant feature subset selection and classification stages. This general three-stage framework is realized using various recognition strategies. In particular, the performance of the developed Vector Supported Convex Hull classification algorithm is compared against Support Vector Machines run in configuration with two different feature selection methods.