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Image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...

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Two-photon calcium imaging (TPCI) is a functional neuroimaging technique that simultaneously reveals the function of small populations of cells as well as the structure of surrounding brain tissue. These unique properties cause TPCI to be increasingly popular for experimental basic neuroscience. Unfortunately, methodological development for data processing has not kept pace with experimental needs. I address this lack by developing and testing new methodology for several key tasks. Specifically, I address two primary analysis steps which are nearly universally required in early data processing: region of interest segmentation and motion correction. For each task I organize the sparse existing literature, clearly define the requirements of the problem, propose a solution, and evaluate it on experimental data. I develop MaSCS, an automated adaptable multi-class segmentation system that improves with use. I carefully define and describe the impact of motion artifacts on imaging data, and quantify the effects of standard and innovative motion correction approaches. Finally, I apply my work on segmentation and motion correction to explore one scientific target, namely discovering correlation-based cell clustering. I show that estimating such correlation-based clustering remains an open question, as it is highly sensitive to motion artifacts, even after motion correction techniques are applied. The contributions of this work include the organization of existing resources, methodological advances in segmentation, motion correction and clustering, and the development of prototype analysis software.

Ultrasound is one of the primary technologies used for clinical purposes. Ultrasound systems have favorable real-time capabilities, are fast and relatively inexpensive, portable and non-invasive. Recent interest in using ultrasound imaging for tongue motion has various applications in linguistic study, speech therapy as well as in foreign language education, where visual-feedback of tongue motion complements conventional audio feedback. Ultrasound images are known to be difficult to recognize. The anatomical structure in them, the rapidity of tongue movements, also missing segments in some frames and the limited frame rate of ultrasound systems have made automatic tongue contour extraction and tracking very challenging and especially hard for real-time applications. Traditional image processing-based approaches have many practical limitations in terms of automation, speed, and accuracy. Recent progress in deep convolutional neural networks has been successfully exploited in a variety of computer vision problems such as detection, classification, and segmentation. In the past few years, deep belief networks for tongue segmentation and convolutional neural networks for the classification of tongue motion have been proposed. However, none of these claim fully-automatic or real-time performance. U-Net is one of the most popular deep learning algorithms for image segmentation, and it is composed of several convolutions and deconvolution layers. In this thesis, we proposed a fully automatic system to extract tongue dorsum from ultrasound videos in real-time using a simplified version of U-Net, which we call sU-Net. Two databases from different machines were collected, and different training schemes were applied for testing the learning capability of the model. Our experiment on ultrasound video data demonstrates that the proposed method is very competitive compared with other methods in terms of performance and accuracy.

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Medical image analysis is a ubiquitous and essential part of modem health care. A crucial first step to this is segmentation, which is often complicated by many factors including subject diversity, pathology, noise corruption, and poor image resolution. Traditionally, manual tracing by experts was done. While considered accurate, this process is time consuming and tedious, especially when performed slice-by-slice on three-dimensional (3D) images over large datasets or on two-dimensional (2D) but topologically complicated images such as a retinography. On the other hand, fully-automated methods are typically faster, but work best with data-dependent, carefully tuned parameters and still require user validation and refinement. This thesis contributes to the field of medical image segmentation by proposing a highly-automated, interactive approach that effectively merges user knowledge and efficient computing. To this end, our work focuses on graph-based methods and offer globally optimal solutions. First, we present a novel method for 3D segmentation based on a 3D Livewire approach. This approach is an extension of the 2D Livewire framework, and this method is capable of handling objects with large protrusions, concavities, branching, and complex arbitrary topologies. Second, we propose a method for efficiently segmenting 2D vascular networks, called ‘Live-Vessel’. Live-Vessel simultaneously extracts vessel centrelines and boundary points, and globally optimizes over both spatial variables and vessel radius. Both of our proposed methods are validated on synthetic data, real medical data, and are shown to be highly reproducible, accurate, and efficient. Also, they were shown to be resilient to high amounts of noise and insensitive to internal parameterization.

Medical image analysis is a ubiquitous and essential part of modem health care. A crucial first step to this is segmentation, which is often complicated by many factors including subject diversity, pathology, noise corruption, and poor image resolution. Traditionally, manual tracing by experts was done. While considered accurate, this process is time consuming and tedious, especially when performed slice-by-slice on three-dimensional (3D) images over large datasets or on two-dimensional (2D) but topologically complicated images such as a retinography. On the other hand, fully-automated methods are typically faster, but work best with data-dependent, carefully tuned parameters and still require user validation and refinement. This thesis contributes to the field of medical image segmentation by proposing a highly-automated, interactive approach that effectively merges user knowledge and efficient computing. To this end, our work focuses on graph-based methods and offer globally optimal solutions. First, we present a novel method for 3D segmentation based on a 3D Livewire approach. This approach is an extension of the 2D Livewire framework, and this method is capable of handling objects with large protrusions, concavities, branching, and complex arbitrary topologies. Second, we propose a method for efficiently segmenting 2D vascular networks, called ‘Live-Vessel’. Live-Vessel simultaneously extracts vessel centrelines and boundary points, and globally optimizes over both spatial variables and vessel radius. Both of our proposed methods are validated on synthetic data, real medical data, and are shown to be highly reproducible, accurate, and efficient. Also, they were shown to be resilient to high amounts of noise and insensitive to internal parameterization.

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