STATISTICAL DETECTION OF BREAST CANCER BY MAMMOGRAM IMAGE

Rajini gk; Shaik Naseera; Saravanan M

doi:10.22159/ajpcr.2017.v10i1.15003

Authors

Rajini gk VIT University
Shaik Naseera Associate Professor, School of Computing Science and Engineering, VIT University, Vellore
Saravanan M VIT university

DOI:

https://doi.org/10.22159/ajpcr.2017.v10i1.15003

Abstract

Objective: To create awareness about the breast cancer which has become one of the most common diseases among women that leads to death if not recognized at early stage.

Methods: The technique of acquiring breast image is called mammography and is a diagnostic and screening tool to detect cancer. A cascade algorithm based on these statistical parameters is implemented on these mammogram images to segregate normal, benign, and malignant diseases.

Results: Statistical features - such as mean, median, standard deviation, perimeter, and skewness - were extracted from mammogram images to describe their intensity and nature of distribution using ImageJ.

Conclusion: A noninvasive technique which includes statistical features to determine and classify normal, benign, and malignant images are identified.

Keywords: Breast cancer, Benign, Malignant, Mammogram image, ImageJ.

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