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Image-guided therapies have the potential to improve the accuracy of treating liver cancer. In order to register intraoperative with preoperative liver images, joint segmentation and registration methods require fast segmentation of matching vessel centerlines. The algorithm presented in this thesis solves this problem by tracking the centerlines using ridge and cross-section information, and uses knowledge of the patient’s vasculature in the preoperative image to ensure correspondence. The algorithm was tested on three MR images of healthy volunteers and one CT image of a patient with liver cancer. Results show that in the context of join segmentation registration, if the registration error is less than 2.0mm, the average segmentation error is 0.73-1.68mm, with 88-100% of the vessels having an error less than a voxel length. For registration error less than 4.6mm, the average segmentation error is 1.17-2.11mm, with 79-98% of the vessels having an error less than a voxel length.

Les agents situés dans l'image fournissent un cadre privilégié pour la mise en oeuvre de stratégies coopératives et localement adaptées en segmentation d'image. Ils facilitent l'intégration des connaissances a priori, expressions d'un modèle, permettant ainsi de dégager de nouvelles contraintes indispensables à toutes les étapes de la vision par ordinateur (de la segmentation à l'interprétation). Nous proposons un cadre conceptuel pour l'architecture logicielle d'un système de vision bas-niveau basée sur des agents situés dans l'image. Une telle architecture est articulée en trois niveaux d'analyse et de description : 1. Description globale et structurelle de l'organisation regroupant les agents. Cette étape de description s'attache à établir les liens entre agents. Nous proposons comme élément organisationnel la pyramide irrégulière qui impose sa structure à la population d'agents, afin de garantir un comportement globalement contrôlable et convergent de ces derniers. 2. Description locale, fonctionnelle et comportementale des agents composant le système. Nous proposons une mise en oeuvre particulière de l'architecture logicielle de vision bas-niveau. Dans cette dernière, deux familles d'agents, qui traduisent des primitives région et contour, interagissent localement au sein de la pyramide. Notre objectif est de montrer comment cette méthodologie permet une implémentation riche, flexible et distribuée des aspects précédemment identifiés; à savoir : l'adaptation locale, l'intégration et l'expression d'incertitudes dans l'information a priori et des traitements coopératifs région/région et région/contour. 3. Finalement, une analyse globale, comparative et fonctionnelle vérifie que l'ensemble des interactions locales produit une bonne segmentation des images. Nous comparons notre approche avec d'autres méthodes de segmentation sur des images médicales et des images de synthèse.

This dissertation focuses on developing imaging informatics algorithms for clinical decision support systems (CDSSs) based on histopathological whole-slide images (WSIs). Currently, histopathological analysis is a common clinical procedure for diagnosing cancer presence, type, and progression. While diagnosing patients using biopsy slides, pathologists manually assess nuclear morphology. However, making decisions manually from a slide with millions of nuclei can be time-consuming and subjective. Researchers have proposed CDSSs that help in decision making but they have limited reproducibility. The development of robust CDSSs for WSIs faces several informatics challenges: (1) Lack of robust segmentation methods for histopathological images, (2) Semantic gap between quantitative information and pathologist’s knowledge, (3) Lack of batch-invariant imaging informatics methods, (4) Lack of knowledge models for capturing informative patterns in large WSIs, and (5) Lack of guidelines for optimizing and validating diagnostic models. I conducted advanced imaging informatics research to overcome these challenges and developed novel methods to extract information from WSIs, to model knowledge embedded in large histopathological datasets, such as The Cancer Genome Atlas (TCGA), and to assist decision making with biological and clinical validation. I validated my methods for two applications: (1) diagnosis of histopathology-based endpoints such as subtype and grade and (2) prediction of clinical endpoints such as metastasis, stage, lymphnode spread, and survival. The statistically emergent feature subsets in the diagnostic models for histopathology-based endpoints were concordant with pathologists’ knowledge.

In brachytherapy, radiation therapy is performed by placing the radiation source into or very close to the tumour. When calculating the absorbed dose, water is often used as the radiation transport and dose scoring medium for soft tissues and this leads to inaccuracies. The iterative reconstruction algorithm DIRA is under development at the Center for Medical Imaging Science and Visualization, Linköping University. DIRA uses dual-energy CT to decompose tissues into different doublets and triplets of base components for a better absorbed dose estimation. To accurately determine mass fractions of these base components for different tissues, the tissues needs to be identified in the image. The aims of this master thesis are: (i) Find an automated segmentation algorithm in CT that best segments the male pelvis. (ii) Implement a segmentation algorithm that can be used in DIRA. (iii) Implement a fully automatic segmentation algorithm. Seven segmentation methods were tested in Matlab using images obtained from Linköping University Hospital. The methods were: active contours, atlas based registration, graph cuts, level set, region growing, thresholding and watershed. Four segmentation algorithms were selected for further analysis: phase based atlas registration, region growing, thresholding and active contours without edges. The four algorithms were combined and supplemented with other image analysis methods to form a fully automated segmentation algorithm that was implemented in DIRA. The newly developed algorithm (named MK2014) was sufficiently stable for pelvic image segmentation with a mean computational time of 45.3 s and a mean Dice similarity coefficient of 0.925 per 512×512 image. The performance of MK2014 tested on a simplified anthropomorphic phantom in DIRA gave promising result. Additional tests with more realistic phantoms are needed to confirm the general applicability of MK2014 in DIRA.

The haptic exploration of 2-D images is a challenging problem in computer haptics. Research on the topic has primarily been focused on the exploration of maps and curves. This thesis describes the design and implementation of a system for the haptic exploration of photographs. The system builds on various research directions related to assistive technology, computer haptics, and image segmentation. An object-level segmentation hierarchy is generated from the source photograph to be rendered haptically as a contour image at multiple levels-of-detail. A tool for the authoring of object-level hierarchies was developed, as well as an innovative type of user interaction by region selection for accurate and efficient image segmentation. According to an objective benchmark measuring how the new method compares with other interactive image segmentation algorithms shows that our region selection interaction is a viable alternative to marker-based interaction. The hierarchy authoring tool combined with precise algorithms for image segmentation can build contour images of the quality necessary for the images to be understood by touch with our system. The system was evaluated with a user study of 24 sighted participants divided in different groups. The first part of the study had participants explore images using haptics and answer questions about them. The second part of the study asked the participants to identify images visually after haptic exploration. Results show that using a segmentation hierarchy supporting multiple levels-of-detail of the same image is beneficial to haptic exploration. As the system gains maturity, it is our goal to make it available to blind users.

Speech Endpoint Detection, also known as Speech Segmentation, is an unsolved problem in speech processing that affects numerous applications including robust speech recognition. This task is not as trivial as it appears, and most of the existing algorithms degrade at low signal-to-noise ratios (SNRs). Most of the previous research approaches have focused on the development of robust algorithms with special attention being paid to the derivation and study of noise robust features and decision rules. This research tackles the endpoint detection problem in a different way, and proposes a novel speech endpoint detection algorithm which has been derived from Chan-Vese algorithm for image segmentation. The proposed algorithm has the ability to fuse multi features extracted from the speech signal to enhance the detection accuracy. The algorithm performance has been evaluated and compared to two widely used speech detection algorithms under various noise environments with SNR levels ranging from 0 dB to 30 dB. Furthermore, the proposed algorithm has also been applied to different types of American English phonemes. The experiments show that, even under conditions of severe noise contamination, the proposed algorithm is more efficient as compared to the reference algorithms.

Classification of the agricultural fields using remote sensing images is one of the most popular methods used for crop mapping. Most recent classification techniques are based on per-field approach that works as assigning a crop label for each field. Commonly, the spatial vector data is used for the boundaries of the fields for applying the classification within them. However, crop variation within the fields is a very common problem. In this case, the existing field boundaries may be insufficient for performing the field-based classification and therefore, image segmentation is needed to be employed to detect these homogeneous segments within the fields. This study proposed a field-based approach to segment the crop fields in an image within the integrated environment of Geographic Information System (GIS) and Remote Sensing. In this method, each field is processed separately and the segments within each field are detected. First, an edge detection is applied to the images, and the detected edges are vectorized to generate the straight line segments. Next, these line segments are correlated with the existing field boundaries using the perceptual grouping techniques to form the closed regions in the image. The closed regions represent the segments each of which contain a distinct crop type. To implement the proposed methodology, a software was developed. The implementation was carried out using the 10 meter spatial resolution SPOT 5 and the 20 meter spatial resolution SPOT 4 satellite images covering a part of Karacabey Plain, Turkey. The evaluations of the obtained results are presented using different band combinations of the images.

Image segmentation has an important role in image processing because it is a tool to obtain higher level object descriptions for further processing. In some applications such as large image databases or video image sequence segmentations, the speed of the segmentation algorithm may become a drawback of the application. This thesis work is a study to improve the run-time performance of a well-known segmentation algorithm, namely the Recursive Shortest Spanning Tree (RSST). Both the original and the fast RSST found in the literature are analyzed and a comparison is made between these techniques. Simple modifications and an alternative link cost structure are proposed and evaluated. Finally, a distributed implementation based on a simple image partitioning strategy is attempted. The thesis presents the results of an extensive computational study with respect to both run-time performance and image segmentation quality.

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