http://hdl.handle.net/2160/23 Wed, 22 May 2013 05:07:46 GMT 2013-05-22T05:07:46Z http://hdl.handle.net/2160/13904 Comparing the performance of a mass detection CAD system when using manual and automatic breast density information, Zwiggelaar, R. International Journal of Computer Assisted Radiology and Surgery All Volumes & Issues Volume 3, Issue 1 Supplement, June 2008 Proceedings of the 22nd International Congress and Exhibition, Barcelona, Spain, June 25-28, 2008 ISSN: 1861-6410 (Print) 1861-6429 (Online) Sun, 01 Jun 2008 00:00:00 GMT http://hdl.handle.net/2160/13904 2008-06-01T00:00:00Z http://hdl.handle.net/2160/12869 The evaluation of effects on breast cancer diagnoses using the mammographic semantic information Qi, Da; Denton, Erika R. E.; Leason, Joanna M. E.; Othman, Diaa; Zwiggelaar, Reyer Krupinski, Elizabeth A. In this paper, we describe the evaluation of the effects of mammographic semantic information in breast cancer diagnoses. A brief description of relations between semantic information and image features are given. We demonstrate the experiments based on mammographic semantic information and the MIAS database. Mammograms were annotated by expert radiologists with semantic information and assigned NHSBSP five-point score. Two classifiers were applied to automatically classify the mammogram into NHSBSP five-point score using the semantic information and radiologists also classified the mammograms by their own annotated semantic information. The analysis of the experimental results provides further understanding when using mammographic semantic information in breast cancer diagnosis. It also indicated a common knowledge base and links between computers and human experts. Tue, 01 Jan 2008 00:00:00 GMT http://hdl.handle.net/2160/12869 2008-01-01T00:00:00Z http://hdl.handle.net/2160/12868 Analysis of using anatomical linear structure information in mammographic risk assessment Zwiggelaar, R. Tue, 01 Jan 2008 00:00:00 GMT http://hdl.handle.net/2160/12868 2008-01-01T00:00:00Z http://hdl.handle.net/2160/12867 Mammographic Segmentation Based on Texture Modelling of Tabár Mammographic Building Blocks He, W.; Zwiggelaar, R.; Denton, E. R. E.; Muhimmah, I. We present an approach to automate texton selection to achieve optimized mammogram segmentation results with respect to mammographic building blocks (i.e. nodular, linear, homogeneous, and radiolucent) as described by Tabár’s tissue model. Such segmentation results are expected to lead to improvements in automatic mammographic risk assessment modelling. The texton selection process has three distinct components, covering a) texton ranking, b) outlier detection, and c) visual assessment. The initial results, on tissue specific regions and full mammographic images are promising, but at the same time indicate shortcomings, which are discussed. Tue, 01 Jan 2008 00:00:00 GMT http://hdl.handle.net/2160/12867 2008-01-01T00:00:00Z