Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Project knole: an autocosmic approach to authoring resonant computational characters

Project knole, consisting of this thesis and a mixed reality installation artwork centred around a computational simulation, is a practice-based response to the question of how a character in a work of computational narrative art might maintain their defining quality of dynamic agency within a system (arguably one of the key potentials of the form), while achieving the ‘resonant’ qualities of characters in more materially-static artforms. In all aspects of this project, I explore a new design philosophy for achieving this balance; between the authorship of a procedural computational system, and the ability of that system to ‘resonate’ with the imagination of an audience. This philosophy, which I term the ‘autocosmic’, seeks inspiration for the curation of audience response outside the obvious boundaries of artistic discipline, across the wider spectrum of human imaginative engagement; examples often drawn from mostly non-aesthetic domains. As well as defining the terms ‘resonance’ and ‘autocosmic’, and delineating my methodology more generally, this thesis demonstrates how the ‘autocosmic’ was employed within my creative work. In particular, it shows how some of the perennial problems of computational character development might be mediated by exploring other non-aesthetic examples of imaginative, narrative engagement with personified systems. In the context of this project, such examples come from the historio-cultural relationship between human beings and the environments they inhabit, outside of formal artistic practice. From this ‘autocosmic’ launchpad, I have developed an artwork that starts to explore how this rich cultural and biological lineage of human social engagement with systemic place can be applied fruitfully to the development of a ‘resonant’ computational character