Detection of synthetic and real microcalcifications based on statistical analysis of original and highpass-filtered mammograms

Authors:
Ikhlas Abdel-Qader;Imad Zyout;Christina Jacobs
Affiliations:
Electrical and Computer Engineering Department, Western Michigan University, MI;Electrical and Computer Engineering Department, Western Michigan University, MI;Radiology Department, Bronson Methodist Hospital, Kalamazoo, MI
Venue:
Machine Graphics & Vision International Journal
Year:
2009

Citing 4
Cited 1

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Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms

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Bayesian classifier with simplified learning phase for detecting microcalcifications in digital mammograms

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Abstract

Detection of clustered microcalcifications in digitized mammograms can be very useful for early detection of breast cancer. Clustered microcalcifications have a distinguished signature in both spatial and frequency domains. In the spatial domain, they appear as white spots which represent local maxima, while in the frequency domain microcalcifications represent local anomalies that can be captured within the high frequency subbands. In this work, we propose an algorithm for detection of clustered microcalcifications by utilizing these signatures, integrating the statistical parameters of both spatial and frequency domains. The results prove the effectiveness of the proposed method, and indicate that the exploitation of both domain signatures of the clustered microcalcifications yields significantly better detection results.