Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier

Author:

Alam NashidORCID,Denton Erika R. E.,Zwiggelaar ReyerORCID

Abstract

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 .

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

Reference67 articles.

1. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

2. International Variation in Female Breast Cancer Incidence and Mortality Rates

3. Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection;Jalalian;EXCLI J.,2017

4. New relationships between breast microcalcifications and cancer

5. Breast Cancer: Early Detection with Mammography. Perception, Interpretation, Histopathologic Correlation;Tabar,2005

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