Hybrid Segmentation of Anatomical Data

We propose new hybrid methods for automated segmentation of radiological patient data and the Visible Human data. In this paper, we integrate boundary-based and region-based segmentation methods which amplifies the strength but reduces the weakness of both approaches. The novelty comes from combining a boundary-based method, the deformable model-based segmentation with region-based segmentation methods, the fuzzy connectedness and Voronoi Diagram-based segmentation, to develop hybrid methods that yield high precision, accuracy and efficiency. This work is a part of a NLM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight)

Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Doctor of Philosophy. Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with the resulting loss of speech. With recent advances in portable computing power, automatic lip-reading (ALR) may become a viable approach to voice restoration. This thesis addresses the image processing aspect of ALR, and focuses three contributions to colour-based lip segmentation. The rst contribution concerns the colour transform to enhance the contrast between the lips and skin. This thesis presents the most comprehensive study to date by measuring the overlap between lip and skin histograms for 33 di erent colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%, and results show that selecting the correct transform can increase the segmentation accuracy by up to three times. The second contribution is the development of a new lip segmentation algorithm that utilises the best colour transforms from the comparative study. The algorithm is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation error (SE) of 7:39 %. The third contribution focuses on the impact of the histogram threshold on the segmentation accuracy, and introduces a novel technique called Adaptive Threshold Optimisation (ATO) to select a better threshold value. The rst stage of ATO incorporates -SVR to train the lip shape model. ATO then uses feedback of shape information to validate and optimise the threshold. After applying ATO, the SE decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp or relative improvement of 15:1%. While this thesis concerns lip segmentation in particular, ATO is a threshold selection technique that can be used in various segmentation applications.MT201

Machine learning techniques in pain recognition.

No abstract available.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b131711

Colour object search

The visual search process is required when locating an object in some region of space. To perform this search two capabilities must be available: the ability to recognise the object when it comes into view; and a way of selecting these views. Visual search is often complicated by object occlusion and low spatial resolutions of the object. Although the human visual system performs this task effortlessly, the mechanisms of it are not properly understood. Object colour and geometry, however do play an important role. This thesis develops an object search methodology which assumes that a computer vision system captures both wide-angle and zoomed images of the scene containing the object. Since most of the research has focused on object recognition using geometry, this system is purely colour-based. It is not expected that object colour will always give a definitive solution, however database pruning will often occur leading to reduced search times. The thesis argues that because colour is salient and more resilient than geometry to decreases in spatial resolution, it is more appropriate for visual search when the object occupies a small spatial resolution in an image with a large field of view. It also demonstrates that colour can be used to recognise objects when they occupy most of the field of view; as well as discriminate between database models with similar colour proportions but different region topologies. These conclusions are supported by the results produced by three algorithms, two of which perform colour object search and one that performs colour object recognition. The first object search algorithm uses image locations containing salient object colours as a method of selecting views. Each of these views are ranked indicating which view most likely contains the object. The second object search algorithm identifies image regions with similar colour and topology as the object. These results are produced in a best-first order. The object recognition algorithm uses an invariant based on region area to identify three corresponding model and image regions. A transformation is calculated to bring the model and object into the same viewpoint where region matches are based on position and colour. Each of these methods produced good results in complex indoor scenes with fluorescence and/or tungsten filament lighting; also the search speeds were impressive

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research