Advances in medical imaging technology have led to the acquisition of large number of images in different modalities. On some of these images the boundaries of key organs need to be accurately identified for treatment planning and diagnosis. This is typically performed manually by a physician who uses prior knowledge of organ shapes and locations to demarcate the boundaries of organs. Such manual segmentation is subjective, time consuming and prone to inconsistency. Automating this task has been found to be very challenging due to poor tissue contrast and ill-defined organ/tissue boundaries. This dissertation presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative location of objects into a single framework in order to perform automated segmentation. The algorithm has been tested on two different datasets: for segmenting hands on thermographic images and for prostate segmentation on pelvic computed tomography (CT) and magnetic resonance (MR) images. In this dissertation we report the results of segmentation in two dimensions (2D) for thermographic images; and two as well as three dimensions (3D) for pelvic images. We show that combining multiple features for segmentation improves segmentation accuracy as compared with segmentation using single features such as texture or shape alone.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Tannenbaum, Allen; Committee Member: Al Regib, Ghassan; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Stillman, Arthur; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 73-78). Also available in electronic version.

Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and di↵erent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Modern cancerous tumor diagnostics is nearly impossible without invasive methods, such as biopsy, that may require involved surgical procedures. In recent years some work has been done to develop alternative non-invasive methods of medical diagnostics. For this purpose, the data obtained from an ultrasound image of the body crosssection, has been analyzed using statistical models, including Rayleigh, Rice, Nakagami, and K statistical distributions. The homodyned-K (H-K) distribution has been found to be a good statistical tool to analyze the envelope and/or the intensity of backscattered signal in ultrasound tissue characterization. However, its use has usually been limited due to the fact that its probability density function (PDF) is not available in closed-form. In this work we present a novel closed-form representation for the H-K distribution. In addition, we propose using the first order approximation of the H-K distribution, the I-K distribution that has a closed-form, for the ultrasound tissue characterization applications. More specifically, we show that some tissue conditions that cause the backscattered signal to have low effective density values, can be successfully modeled by the I-K PDF. We introduce the concept of using H-K PDF-based and I-K PDF-based entropies as additional tools for characterization of ultrasonic breast tissue images. The entropy may be used as a goodness of fit measure that allows to select a better-fitting statistical model for a specific data set. In addition, the values of the entropies as well as the values of the statistical distribution parameters, allow for more accurate classification of tumors. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

Computer vision approximates human vision using computers. Two subsets are explored in this work: image segmentation and visual tracking. Segmentation involves partitioning an image into logical parts, and tracking analyzes objects as they change over time. The presented research explores a key hypothesis: localizing analysis of visual information can improve the accuracy of segmentation and tracking results. Accordingly, a new class of segmentation techniques based on localized analysis is developed and explored. Next, these techniques are applied to two challenging problems: neuron bundle segmentation in diffusion tensor imagery (DTI) and plaque detection in computed tomography angiography (CTA) imagery. Experiments demonstrate that local analysis is well suited for these medical imaging tasks. Finally, a visual tracking algorithm is shown that uses temporal localization to track objects that change drastically over time.

Thesis (M. E.) (Honours) -- University of Western Sydney, 2002. / Thesis submitted in fulfilment of the requirements for the degree of Master of Engineering (Honours), University of Western Sydney, School of Engineering & Industrial Design, March 2002. Bibliography : p. 99-104.

Thesis (Ph.D.). / Written for the Dept. of Electrical & Computer Engineering. Title from title page of PDF (viewed 2008/01/12). Includes bibliographical references.

Thesis (Ph. D.)--University of Sydney, 2005. / Title from title screen (viewed 19 May 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Dept. of Pathology, Faculty of Medicine. Degree awarded 2005; thesis submitted 2004. Includes bibliographical references. Also available in print form.