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The Role of Self-Similarity for Computer Aided Detection Based on Mammogram Analysis
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Author(s): Filipe Soares (University of Beira Interior & Siemens S.A. Healthcare Sector, Portugal), Mário M. Freire (University of Beira Interior, Portugal), Manuela Pereira (University of Beira Interior, Portugal), Filipe Janela (Siemens S.A. Healthcare Sector, Portugal)and João Seabra (Siemens S.A. Healthcare Sector, Portugal)
Copyright: 2011
Pages: 19
Source title:
Biomedical Diagnostics and Clinical Technologies: Applying High-Performance Cluster and Grid Computing
Source Author(s)/Editor(s): Manuela Pereira (University of Beira Interior, Portugal)and Mario Freire (University of Beira Interior, Portugal)
DOI: 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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