AUTOMATIC FEATURE EXTRACTION FOR BREAST DENSITY SEGMENTATION AND CLASSIFICATION


Aswathy K Cherian, Poovammal E, Malathy C

Abstract


Objective: Cancer is the uncontrollable multiplication of cells in human body. The expansion of cancerous cells in the breast area of the women is identified as breast cancer. It is mostly identified among women aged above 40. With the current advancement in the medical field, various automatic tests are available for the identification of cancerous tissues. The cancerous cells are spotted by taking the photo imprint in the form of X-ray comprising the breast area of the woman. Such images are called mammograms. Segmentation of mammograms is the primary step toward diagnosis. It involves the pre-processing of the image to identify the region of interest (ROI). Later, features are extracted from the image which involves the learned features that may be statistical and textural features [7]. When these features are used as input to the simple classifier, it helps us to predict the risk of cancer. The support vector machine (SVM) classifier was proved to produce a better accuracy percentage with the features extracted.

Methods: The mammograms are subjected to a pre-processing stage, where the images are processed to identify the ROI. Next, the features are extracted from these images to identify the statistical [9] and textural features. Finally, these features are used as input to the simple classifier, it helps us to predict the risk of cancer.

Results: The SVM classifier was proved to produce the maximum accuracy of about 88.67% considering 13 features including both statistical and textural features. The features taken for the study are mean, inverse difference moment, energy, entropy, root mean square, correlation, homogeneity, variance, skewness, range, contrast, kurtosis, and smoothness.

Conclusion: Computer-aided diagnosis is one of the most common methods of detection of cancer with mammograms, and it involves minor human intervention. The dataset of mammograms was analyzed and found that SVM provided the highest accuracy of 88.67%. A wide range of the study is progressing in the field of cancer as this disease causes a high threat of human life in this era.


Keywords


Cancer, Segmentation, Feature extraction, Mammograms, Classification, Gray-level co-occurrence matrix, Region of interest.

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About this article

Title

AUTOMATIC FEATURE EXTRACTION FOR BREAST DENSITY SEGMENTATION AND CLASSIFICATION

Keywords

Cancer, Segmentation, Feature extraction, Mammograms, Classification, Gray-level co-occurrence matrix, Region of interest.

DOI

10.22159/ajpcr.2017.v10i12.19699

Date

01-12-2017

Additional Links

Manuscript Submission

Journal

Asian Journal of Pharmaceutical and Clinical Research
Vol 10 Issue 12 December 2017 Page: 111-115

Print ISSN

0974-2441

Online ISSN

2455-3891

Statistics

15 Views | 27 Downloads

Authors & Affiliations

Aswathy K Cherian
Department of Computer Science and Engineering, SRM University, Kattankulathur - 603 203, Kancheepuram, Tamil Nadu, India.
India

Poovammal E
Department of Computer Science and Engineering, SRM University, Kattankulathur - 603 203, Kancheepuram, Tamil Nadu, India.
India

Malathy C
Department of Computer Science and Engineering, SRM University, Kattankulathur - 603 203, Kancheepuram, Tamil Nadu, India.
India


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