Alam, Nashid

Denton, Erika R. E.

Zwiggelaar, Reyer

English

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (95.00 ± 0.57%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01.</p

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Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier

https://pure.aber.ac.uk/portal/files/30346109/Nashid_JOI_Final.pdf

Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier

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