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Vol. 190, Issue 7, July 2015, pp. 53-57




Mammographic Image Analysis of Breast Using Neural Network


School of Electrical Engineering, VIT University, Vellore - 632014, Tamil Nadu, India

E-mail: ishuma@gmail.com


Received: 19 May 2015 /Accepted: 30 June 2015 /Published: 31 July 2015

Digital Sensors and Sensor Sysstems


Abstract: This paper discusses the various stages of detecting tumours of the breast mammogram images. A Neural Network algorithm is applied for obtaining the complete classification of the tumour into normal or abnormal. The most important procedure or technique for obtaining the classification is the feature extraction, by extracting a few of discriminative features, first-order statistical intensities and gradients. The Image Pre-processing technique is essential prior to Image Segmentation in order to obtain accurate segmentation. Thus mass detection can be carried out. The processes involved in achieving the three techniques mentioned above include global equalization transformation, denoising, binarization, breast orientation determination and the pectoral muscle suppression. The presented feature difference matrices could be created by five features extracted from a suspicious region of interest (ROI). Grey Level Co-occurrence Matrix (GLCM) aids the obtaining of statistical features such as correlation, energy, entropy and homogeneity. The other statistical to features to obtain are area, moment, variance, entropy, standard deviation and moment. The Neural network technique yields results of abnormal mammograms.


Keywords: Digital mammography, Pre-processing, Pectoral muscle suppression, Segmentation, Feature extraction, Gray-level co-occurrence matrix, Linear classifier.


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