article information

Iranian Journal of Radiology: Vol.12, issue 3; e91069
published online: July 22, 2015
article type: Research Article
received: April 20, 2013
revised: July 21, 2013
accepted: December 15, 2013
DOI: https://doi.org/10.5812/iranjradiol.11656
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Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data

authors:

avatar Hossein Yousefibanaem 1 , avatar Alireza Mehri Dehnavi 1 , 2 , * , avatar Makhtum Shahnazi 3

Department of Biomedical Engineering, Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
School of Optometry and Visual Science, University of Waterloo, Waterloo, Canada
Department of Radiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

how to cite: Yousefibanaem H, Mehri Dehnavi A, Shahnazi M. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data. I J Radiol. 2015;12(3):e91069. https://doi.org/10.5812/iranjradiol.11656.

Abstract

Background: Breast cancer is one of the most encountered cancers in women. Detection and classification of the cancer into malignant or benign is one of the challenging fields of the pathology.
Objectives: Our aim was to classify the mammogram data into normal and abnormal by ensemble classification method.
Patients and Methods: In this method, we first extract texture features from cancerous and normal breasts, using the Gray-Level Co-occurrence Matrices (GLCM) method. To obtain better results, we select a region of breast with high probability of cancer occurrence before feature extraction. After features extraction, we use the maximum difference method to select the features that have predominant difference between normal and abnormal data sets. Six selected features served as the classifying tool for classification purpose by the proposed ensemble supervised algorithm. For classification, the data were first classified by three supervised classifiers, and then by simple voting policy, we finalized the classification process.
Results: After classification with the ensemble supervised algorithm, the performance of the proposed method was evaluated by perfect test method, which gave the sensitivity and specificity of 96.66% and 97.50%, respectively.
Conclusions: In this study, we proposed a new computer aided diagnostic tool for the detection and classification of breast cancer. The obtained results showed that the proposed method is more reliable in diagnostic to assist the radiologists in the detection of abnormal data and to improve the diagnostic accuracy.

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