Texture Classification using Fractal Geometry for the Diagnosis of Skin Cancers
| dc.contributor.author | Blackledge, J. M. | en_US |
| dc.contributor.author | Dubovitskiy, D. A. | en_US |
| dc.contributor.editor | Wen Tang and John Collomosse | en_US |
| dc.date.accessioned | 2014-01-31T20:06:40Z | |
| dc.date.available | 2014-01-31T20:06:40Z | |
| dc.date.issued | 2009 | en_US |
| dc.description.abstract | We present an approach to object detection and recognition in a digital image using a classification method that is based on the application of a set of features that include fractal parameters such as the Lacunarity and Fractal Dimension. The principal issues associated with object recognition are presented and a self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory considered. The methods discussed, and the 'system' developed, have a range of applications in 'machine vision' and in this publication, we focus on the development and implementation of a skin cancer screening system that can be used in a general practice by non-experts to 'filter' normal from abnormal cases so that in the latter case, a patient can be referred to a specialist. The paper provides an overview of the system design and includes a link from which interested readers can download and use a demonstration version of the system developed to date. | en_US |
| dc.description.seriesinformation | Theory and Practice of Computer Graphics | en_US |
| dc.identifier.isbn | 978-3-905673-71-5 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/LocalChapterEvents/TPCG/TPCG09/041-048 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): F.2.2; I.5.4 [Analysis of Algorithms and problem complexity, Pattern Recognition]: Pattern matching, Computer vision | en_US |
| dc.title | Texture Classification using Fractal Geometry for the Diagnosis of Skin Cancers | en_US |
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