Ekstraksi Ciri Polip dan Pendarahan Berdasarkan Citra Endoskopi Kolorektal

Cancer is one of the main causes of mortality in the world. Colorectal cancer, also known as colon cancer, is a malignant tumor of the colon and rectum that begins with a polyp. Early inspection is needed to prevent and cure of colorectal cancer because in the early stages colorectal cancer showed no symptoms. At this time the development of information technology allows the quick information retrieval from an image. The aim of this research is to produce a preliminary work in the stages of information analyzing on colorectal endoscopic image extraction result in the form of polyp and bleeding by utilizing extraction technique of image information based on shape and texture. This research aimed can be the basis for the development of colorectal cancer detection system framework. The research that has been carried out gives result of characteristics that can be differentiate between colon bleeding, colon polyp, and normal colon conditions, they are aspect ratio, triangle, correlation, and energy

Geodesic tractography segmentation for directional medical image analysis

Acknowledgements page removed per author's request, 01/06/2014.Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes. This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches.Ph.D.Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patrici

Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology

Liver surgical resection is one of the most frequently used curative therapies. However, resectability is problematic. There is a need for a computer-assisted surgical planning and simulation system which can accurately and efficiently simulate the liver, vessels and tumours in actual patients. The present project describes the development of these core segmentation and deformable modelling techniques. For precise detection of irregularly shaped areas with indistinct boundaries, the segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets. To improve efficiency, a chessboard distance transform was used to replace part of the GVF effort. To automatically initialize the liver volume detection process, a rotating template was introduced to locate the starting slice. For shape maintenance during the segmentation process, a simplified object shape learning step was introduced to avoid occasional significant errors. Skeletonization with fuzzy connectedness was used for vessel segmentation. To achieve real-time interactivity, the deformation regime of this system was based on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh refinement to raise the deformation accuracy and the mesh control quality. This method was now extended to a multiple soft-tissue constraint system, by supplementing it with an adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide comparison of classic measures. Adjustable feature and parameter settings were thus provided, to make tissues of interest distinct from adjacent structures, keeping the mesh suitable for on-line topological transformation and deformation. More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver datasets were tested to evaluate the performance of the segmentation method. Instrument manipulations of probing, grasping, and simple cutting were successfully simulated on deformable constraint liver tissue models. This project was implemented in conjunction with the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was judged satisfactory by the consultant hepatic surgeon

Deep weakly-supervised learning methods for classification and localization in histology images: a survey

Using state-of-the-art deep learning models for cancer diagnosis presents several challenges related to the nature and availability of labeled histology images. In particular, cancer grading and localization in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. In this survey, deep weakly-supervised learning (WSL) models are investigated to identify and locate diseases in histology images, without the need for pixel-level annotations. Given training data with global image-level labels, these models allow to simultaneously classify histology images and yield pixel-wise localization scores, thereby identifying the corresponding regions of interest (ROI). Since relevant WSL models have mainly been investigated within the computer vision community, and validated on natural scene images, we assess the extent to which they apply to histology images which have challenging properties, e.g. very large size, similarity between foreground/background, highly unstructured regions, stain heterogeneity, and noisy/ambiguous labels. The most relevant models for deep WSL are compared experimentally in terms of accuracy (classification and pixel-wise localization) on several public benchmark histology datasets for breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS. Furthermore, for large-scale evaluation of WSL models on histology images, we propose a protocol to construct WSL datasets from Whole Slide Imaging. Results indicate that several deep learning models can provide a high level of classification accuracy, although accurate pixel-wise localization of cancer regions remains an issue for such images. Code is publicly available.Comment: 35 pages, 18 figure

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Image Analysis and Processing With Applications in Proteomics and Medicine

Στην παρούσα διατριβή παρουσιάζονται αυτόματοι αλγόριθμοι ανάλυσης εικόνας για την κατάτμηση διαφόρων τύπων εικόνων, με έμφαση στις εικόνες πρωτεομικής και στις ιατρικές εικόνες. Οι προτεινόμενοι αλγόριθμοι βασίζονται στις αρχές των παραμορφώσιμων μοντέλων. Η διατριβή εστιάζει σε δύο κυρίως στόχους: 1) στην επίλυση του σημαντικού προβλήματος της αυτόματης παραμετροποίησης στην κατάτμηση εικόνας, 2) στην διατύπωση ενός ολοκληρωμένου μοντέλου κατάτμησης εικόνων πρωτεομικής. Η πρώτη συνεισφορά είναι ένα πρωτότυπο πλαίσιο αυτόματης παραμετροποίησης των ενεργών περιγραμμάτων περιοχής. Το πλαίσιο εμπλουτίζει τα αποτελέσματα με αντικειμενικότητα και απελευθερώνει τους τελικούς χρήστες από την επίπονη διαδικασία της εμπειρικής ρύθμισης. Εφαρμόζεται σε διάφορους τύπους ιατρικών εικόνων και παραμένει ανεπηρέαστο στις τροποποιήσεις των ρυθμίσεων των συσκευών λήψης των εικόνων αυτών. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο πλαίσιο διατηρεί υψηλή την ποιότητα κατάτμησης, συγκρίσιμη με εκείνη που επιτυγχάνεται με εμπειρική παραμετροποίηση. Η δεύτερη συνεισφορά είναι ένα αυτόματο μοντέλο βασιζόμενο στα ενεργά περιγράμματα για την κατάτμηση εικόνων πρωτεομικής. Το μοντέλο αντιμετωπίζει σημαντικά προβλήματα συμπεριλαμβανομένων των γραμμών, τεχνουργημάτων, αχνών και επικαλυπτομένων κηλίδων. Ακόμη, παρέχει εναλλακτική λύση στην επιρρεπή σε σφάλματα διαδικασία της χειρωνακτικής επεξεργασίας που απαιτείται στα υπάρχοντα πακέτα λογισμικού. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο μοντέλο υπερτερεί των υπαρχόντων πακέτων λογισμικού σε ποσοτικές μετρικές εντοπισμού και κατάτμησης.This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models. Two objectives are pursued: 1) the core issue of unsupervised parameterization in image segmentation, 2) the formulation of a complete model for the segmentation of proteomics images. The first contribution is a novel framework for automated parameterization of region-based active contours. The presented framework endows segmentation results with objectivity and sets domain users free from the cumbersome process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of acquisition devices. The experimental results demonstrate that the presented framework maintains a high segmentation quality, comparable to the one obtained with empirical parameterization. The second contribution is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues including streaks, artifacts, faint and overlapping spots. Moreover, it provides an alternate to the error-prone process of manual editing, required in state-of-the-art software packages. The experimental results demonstrate that the proposed model outperforms software packages in terms of detection and segmentation quantity metrics

image analysis and processing with applications in proteomics and medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours. The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization. The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Registration of prone and supine CT colonography images and its clinical application

Computed tomographic (CT) colonography is a technique for detecting bowel cancer and potentially precancerous polyps. CT imaging is performed on the cleansed and insufflated bowel in order to produce a virtual endoluminal representation similar to optical colonoscopy. Because fluids and stool can mimic pathology, images are acquired with the patient in both prone and supine positions. Radiologists then match endoluminal locations visually between the two acquisitions in order to determine whether pathology is real or not. This process is hindered by the fact that the colon can undergo considerable deformation between acquisitions. Robust and accurate automated registration between prone and supine data acquisitions is therefore pivotal for medical interpretation, but a challenging problem. The method proposed in this thesis reduces the complexity of the registration task of aligning the prone and supine CT colonography acquisitions. This is done by utilising cylindrical representations of the colonic surface which reflect the colon's specific anatomy. Automated alignment in the cylindrical domain is achieved by non-rigid image registration using surface curvatures, applicable even when cases exhibit local luminal collapses. It is furthermore shown that landmark matches for initialisation improve the registration's accuracy and robustness. Additional performance improvements are achieved by symmetric and inverse-consistent registration and iteratively deforming the surface in order to compensate for differences in distension and bowel preparation. Manually identified reference points in human data and fiducial markers in a porcine phantom are used to validate the registration accuracy. The potential clinical impact of the method has been evaluated using data that reflects clinical practise. Furthermore, correspondence between follow-up CT colonography acquisitions is established in order to facilitate the clinical need to investigate polyp growth over time. Accurate registration has the potential to both improve the diagnostic process and decrease the radiologist's interpretation time. Furthermore, its result could be integrated into algorithms for improved computer-aided detection of colonic polyps