Intelligent computing applications based on eye gaze : their role in mammographic interpretation training

Early breast cancer in women is best identified through high quality mammographic screening. This is achieved by well trained health professionals and appropriate imaging. Traditionally this has used X-ray film but is rapidly changing to utilise digital imaging with the resultant mammograms visually examined on high resolution clinical workstations. These digital images can also be viewed on a range of display devices, such as standard computer monitors or PDAs. In this thesis the potential of using such non-clinical workstation display devices for training purposes in breast screening has been investigated. The research introduces and reviews breast screening both in the UK and internationally where it concentrates upon China which is beginning screening. Various imaging technologies used to examine the breast are described, concentrating upon the move from using X-ray film to digital mammograms. Training in screening in the UK is detailed and it is argued that there is a need to extend this. Initially, a national survey of all UK mammography screeners within the National Health Breast Screening Programme (NHSBSP) was undertaken. This highlighted the current main difficulties of mammographic (film) interpretation training being tied to the device for inspecting these images. The screeners perceived the need for future digital imaging training that could be outside the breast screening centre; namely 3W training (Whatever training required, Whenever and Wherever). This is largely because the clinical workstations would logistically not be available for training purposes due to the daily screening demand. Whilst these workstations must be used for screening and diagnostic purposes to allow visualisation of very small detail in the images, it is argued here that training to identify such features can be undertaken on other devices where there is not the time constraints that exist during breast screening. A series of small pilot studies were then undertaken, trialling experienced radiologists with potential displays (PDAs and laptops) for mammographic image examination. These studies demonstrated that even on a PDA small mammographic features could be identified, albeit with difficulty, even with a very limited HCI manipulation tool. For training purposes the laptop, studied here with no HCI tool, was supported. Such promising results of display acceptability led to an investigation of mammographic inspection on displays of various sizes and resolutions. This study employed radiography students, potentially eventual screeners, who were eye tracked as they examined images on various sized displays. This showed that it could be possible to use a small PDA to deliver training. A detailed study then investigated whether aspects of an expert radiologist s visual inspection behaviour could be used to develop various training approaches. Four approaches were developed and examined using naïve observers who were eye tracked as they were trained and tested. The approaches were found to be all feasible to implement but of variable usefulness for delivering mammographic interpretation training; this was confirmed by opinions from a focus group of screeners. On the basis of the previous studies, over a period of eight months, a large scale study involving 15 film readers from major breast screening centres was conducted where they examined series of digital mammograms on a clinical workstation, monitor and an iPhone. Overall results on individuals performance, image manipulation behaviour and visual search data indicated that a standard monitor could be employed successfully as an alternative for the digital workstation to deliver on-demand mammographic interpretation training using the full mammographic case images. The small iPhone, elicited poor performance, and was therefore judged not suitable for delivering training with the software employed here. However, future software developments may well overcome its shortcomings. The potential to implement training in China was examined by studying the current skill level of some practicing radiologists and an examination of how they responded to the developed training approaches. Results suggest that such an approach would be also applicable in other countries with different levels of screening skills. On-going further work is also discussed: the improvement of performance evaluation in mammography; new visual research on other breast imaging modalities and using visual search with computer aided detection to assist mammographic interpretation training.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

New approaches to the analysis of eye movement behaviour across expertise while viewing brain MRIs

Abstract Brain tumour detection and diagnosis requires clinicians to inspect and analyse brain magnetic resonance images. Eye-tracking is commonly used to examine observers’ gaze behaviour during such medical image interpretation tasks, but analysis of eye movement sequences is limited. We therefore used ScanMatch, a novel technique that compares saccadic eye movement sequences, to examine the effect of expertise and diagnosis on the similarity of scanning patterns. Diagnostic accuracy was also recorded. Thirty-five participants were classified as Novices, Medics and Experts based on their level of expertise. Participants completed two brain tumour detection tasks. The first was a whole-brain task, which consisted of 60 consecutively presented slices from one patient; the second was an independent-slice detection task, which consisted of 32 independent slices from five different patients. Experts displayed the highest accuracy and sensitivity followed by Medics and then Novices in the independent-slice task. Experts showed the highest level of scanning pattern similarity, with medics engaging in the least similar scanning patterns, for both the whole-brain and independent-slice task. In the independent-slice task, scanning patterns were the least similar for false negatives across all expertise levels and most similar for experts when they responded correctly. These results demonstrate the value of using ScanMatch in the medical image perception literature. Future research adopting this tool could, for example, identify cases that yield low scanning similarity and so provide insight into why diagnostic errors occur and ultimately help in training radiologists

Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer

The goal of medical imaging is to acquire and display images of human anatomy and function such that they can be optimally interpreted by a trained observer, e.g., a radiologist. Start-of-art medical image quality is measured by the performance of an observer on a given clinical task. Since psychophysical studies are resource intensive, model observers are widely used as a surrogate in task-based assessment of image quality. Model observers are typically designed to detect at most one abnormality, e.g., a single lesion. However, in clinical practice, there may be multiple abnormalities in a single set of images, which can have a significant impact on treatment planning and outcomes. For example, patients with multifocal and multicentric breast cancer (MFMC), i.e., the presence of two or more tumor foci within the same breast, are more likely to undergo mastectomy rather than breast conservation therapy. Detecting multiple breast tumors is challenging because the prevalence of tumors varies significantly across breast regions, and radiologists do not know the number or location of tumors a priori. The vision of this dissertation is that digital breast tomosynthesis (DBT) has the potential to improve the detection of MFMC, and may offer advantages such as fewer false-positive findings, lower cost, and better accessibility. This dissertation focuses on the design and applications of a model observer to optimize DBT system geometries for detection of multiple breast tumors. This is significant and innovative because prior efforts to optimize DBT image quality only considered unifocal breast cancer scenarios. We highlight the following two main aspects of contributions in this dissertation: (1) We have developed a novel model observer that detects multiple abnormalities in anatomical backgrounds. (2) We have employed the extended 3D multi-lesion model observer to identify DBT system geometries that are most effective for the detection of MFMC. Our results demonstrate that the presence of more than one tumor present distinct challenges to DBT optimization, and that DBT geometries that yield images that are informative for the task of detecting unifocal breast cancer may not necessarily be informative for the task of detecting MFMC. We are validating the clinical relevance of our model observer studies with an ongoing human observer study with experienced breast imaging radiologists.Electrical and Computer Engineerin

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further