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The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis

The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis

Filipe Soares, Mário M. Freire, Manuela Pereira, Filipe Janela, João Seabra
Copyright: © 2011 |Pages: 19
ISBN13: 9781605662800|ISBN10: 1605662801|EISBN13: 9781605662817
DOI: 10.4018/978-1-60566-280-0.ch006
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MLA

Soares, Filipe, et al. "The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis." Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing, edited by Manuela Pereira and Mario Freire, IGI Global, 2011, pp. 181-199. https://doi.org/10.4018/978-1-60566-280-0.ch006

APA

Soares, F., Freire, M. M., Pereira, M., Janela, F., & Seabra, J. (2011). The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis. In M. Pereira & M. Freire (Eds.), Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing (pp. 181-199). IGI Global. https://doi.org/10.4018/978-1-60566-280-0.ch006

Chicago

Soares, Filipe, et al. "The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis." In Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing, edited by Manuela Pereira and Mario Freire, 181-199. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60566-280-0.ch006

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Abstract

The improvement on Computer Aided Detection (CAD) systems has reached the point where it is offered extremely valuable information to the clinician, for the detection and classification of abnormalities at the earliest possible stage. This chapter covers the rapidly growing development of self-similarity models that can be applied to problems of fundamental significance, like the Breast Cancer detection through Digital Mammography. The main premise of this work was related to the fact that human tissue is characterized by a high degree of self-similarity, and that property has been found in medical images of breasts, through a qualitative appreciation of the existing self-similarity nature, by analyzing their fluctuations at different resolutions. There is no need to image pattern comparison in order to recognize the presence of cancer features. One just has to compare the self-similarity factor of the detected features that can be a new attribute for classification. In this chapter, the mostly used methods for self-similarity analysis and image segmentation are presented and explained. The self-similarity measure can be an excellent aid to evaluate cancer features, giving an indication to the radiologist diagnosis.

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