Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Abdel-Qader, Ikhlas; Shen, Lixin; Jacobs, Christina; Abu Amara, Fadi; Pashaie-Rad, Sarah

doi:https://doi.org/10.1155/IJBI/2006/57850

International Journal of Biomedical Imaging

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Recent Advances in Mathematical Methods for the Analysis of Biomedical Images

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Volume 2006 | Article ID 057850 | https://doi.org/10.1155/IJBI/2006/57850

Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Ikhlas Abdel-Qader,¹Lixin Shen,²Christina Jacobs,³Fadi Abu Amara,¹and Sarah Pashaie-Rad⁴

Received22 Jan 2006

Revised20 May 2006

Accepted06 Jun 2006

Published11 Jul 2006

Abstract

Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database.

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Copyright

Copyright © 2006 Ikhlas Abdel-Qader et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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