A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms | Zendy

Xiaoyong Zhang | Zendy; Noriyasu Homma | Zendy; Shotaro Goto | Zendy; Yosuke Kawasumi | Zendy; Tadashi Ishibashi | Zendy; Makoto Abe | Zendy; Norihiro Sugita | Zendy; Makoto Yoshizawa | Zendy

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A Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification Clusters in Mammograms

Author(s) -

Xiaoyong Zhang,

Noriyasu Homma,

Shotaro Goto,

Yosuke Kawasumi,

Tadashi Ishibashi,

Makoto Abe,

Norihiro Sugita,

Makoto Yoshizawa

Publication year - 2013

Publication title -

journal of medical engineering

Language(s) - English

Resource type - Journals

eISSN - 2314-5137

pISSN - 2314-5129

DOI - 10.1155/2013/615254

Subject(s) - microcalcification , artificial intelligence , computer science , pattern recognition (psychology) , wavelet , computer vision , feature (linguistics) , mammography , wavelet transform , breast cancer , image processing , image (mathematics) , cancer , medicine , linguistics , philosophy

The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a multilevel wavelet reconstruction approach. Finally, MCs are detected based on their distributions feature. Experiments are performed on 138 clinical mammograms. The proposed method is capable of detecting 92.9% of true microcalcification clusters with an average of 0.08 false microcalcification clusters detected per image.

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