Designing a secure ubiquitous mammography consultation system

This thesis attempts to design and develop a prototype for mammography image consultation that can work securely within a ubiquitous environment. Mammogram images differ largely from other type of images and it requires special and dedicated techniques to identify the required regions of interest. Thus in Chapter 2 we started to explore the affectivity of the various traditional techniques based on convolution operators (e.g. Sobol, Pretwitt, Canny) for mammography edge detection. The second part of chapter 2 tries to enhance the results obtained via the traditional techniques by hybriding some of them. The hybriding technique is called in our thesis as Pipelined Operators. In this direction we proposed four pipeline operators, which contribute to the edge enhancement as well as abnormalities rendering through the introduction of an additional coloring mechanism. Although the visualization pipelines represent in our view an advancement on the traditional techniques applied to mammograms, such pipelines expose healthcare users to further usage complexities. For this purpose we extended our research work in chapter 2 to find a better single technique that can work smoothly within the healthcare system. In this direction, we developed in the third part of chapter 2 a novel technique for finding edges based on analyzing the dynamic and fuzzy nature of edges in mammograms. We called our developed method as "Dynamic Fuzzy Classifier or the DFC"

Visual tracking: detecting and mapping occlusion and camouflage using process-behaviour charts

Visual tracking aims to identify a target object in each frame of an image sequence. It presents an important scientific problem since the human visual system is capable of tracking moving objects in a wide variety of situations. Artificial visual tracking systems also find practical application in areas such as visual surveillance, robotics, biomedical image analysis, medicine and the media. However, automatic visual tracking algorithms suffer from two common problems: occlusion and camouflage. Occlusion arises when another object, usually with different features, comes between the camera and the target. Camouflage occurs when an object with similar features lies behind the target and makes the target invisible from the camera’s point of view. Either of these disruptive events can cause a tracker to lose its target and fail. This thesis focuses on the detection of occlusion and camouflage in a particle-filter based tracking algorithm. Particle filters are commonly used in tracking. Each particle represents a single hypothesis as to the target’s state, with some probability of being correct. The collection of particles tracking a target in each frame of an image sequence is called a particle set. The configuration of that particle set provides vital information about the state of the tracker. The work detailed in this thesis presents three innovative approaches to detecting occlusion and/or camouflage during tracking by evaluating the fluctuating behaviours of the particle set and detecting anomalies using a graphical statistical tool called a process-behaviour chart. The information produced by the process-behaviour chart is then used to map out the boundary of the interfering object, providing valuable information about the viewed environment. A method based on the medial axis of a novel representation of particle distribution termed the Particle History Image was found to perform best over a set of real and artificial test sequences, detecting 90% of occlusion and 100% of camouflage events. Key advantages of the method over previous work in the area are: (1) it is less sensitive to false data and less likely to fire prematurely; (2) it provides a better representation of particle set behaviour by aggregating particles over a longer time period and (3) the use of a training set to parameterise the process-behaviour charts means that comparisons are being made between measurements that are both made over extended time periods, improving reliability

Texture-boundary detection in real-time

Boundary detection is an essential first-step for many computer vision applications. In practice, boundary detection is difficult because most images contain texture. Normally, texture-boundary detectors are complex, and so cannot run in real-time. On the other hand, the few texture boundary detectors that do run in real-time leave much to be desired in terms of quality. This thesis proposes two real-time texture-boundary detectors – the Variance Ridge Detector and the Texton Ridge Detector – both of which can detect high-quality texture-boundaries in real-time. The Variance Ridge Detector is able to run at 47 frames per second on 320 by 240 images, while scoring an F-measure of 0.62 (out of a theoretical maximum of 0.79) on the Berkeley segmentation dataset. The Texton Ridge Detector runs at 10 frames per second but produces slightly better results, with an F-measure score of 0.63. These objective measurements show that the two proposed texture-boundary detectors outperform all other texture-boundary detectors on either quality or speed. As boundary detection is so widely-used, this development could induce improvements to many real-time computer vision applications

Visual tracking: detecting and mapping occlusion and camouflage using process-behaviour charts

Visual tracking aims to identify a target object in each frame of an image sequence. It presents an important scientific problem since the human visual system is capable of tracking moving objects in a wide variety of situations. Artificial visual tracking systems also find practical application in areas such as visual surveillance, robotics, biomedical image analysis, medicine and the media. However, automatic visual tracking algorithms suffer from two common problems: occlusion and camouflage. Occlusion arises when another object, usually with different features, comes between the camera and the target. Camouflage occurs when an object with similar features lies behind the target and makes the target invisible from the camera’s point of view. Either of these disruptive events can cause a tracker to lose its target and fail. This thesis focuses on the detection of occlusion and camouflage in a particle-filter based tracking algorithm. Particle filters are commonly used in tracking. Each particle represents a single hypothesis as to the target’s state, with some probability of being correct. The collection of particles tracking a target in each frame of an image sequence is called a particle set. The configuration of that particle set provides vital information about the state of the tracker. The work detailed in this thesis presents three innovative approaches to detecting occlusion and/or camouflage during tracking by evaluating the fluctuating behaviours of the particle set and detecting anomalies using a graphical statistical tool called a process-behaviour chart. The information produced by the process-behaviour chart is then used to map out the boundary of the interfering object, providing valuable information about the viewed environment. A method based on the medial axis of a novel representation of particle distribution termed the Particle History Image was found to perform best over a set of real and artificial test sequences, detecting 90% of occlusion and 100% of camouflage events. Key advantages of the method over previous work in the area are: (1) it is less sensitive to false data and less likely to fire prematurely; (2) it provides a better representation of particle set behaviour by aggregating particles over a longer time period and (3) the use of a training set to parameterise the process-behaviour charts means that comparisons are being made between measurements that are both made over extended time periods, improving reliability

Texture edge detection using the compass operator

The compass operator has proven to be a useful tool for the detection of color edges in real images. Its fundamental contribution is the comparison of oriented distributions of image features over a local area at each pixel. This paper presents extensions and modifications to the operator to make it applicable to texture edge detection in high dimensional images whose dimensions represent the output of a texture filter bank. The results show that the extended compass operator can robustly locate edges in natural scenes with complex textures. In addition, the use of a dynamic time warping distribution matching metric and jittered application of the operator improves the computational running time by a factor of over 50 while still producing comparable results. This large-scale speedup makes application of the algorithm to an entire image database computationally feasible.