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Bonfring International Journal of Advances in Image Processing

Impact Factor: 0.245 | International Scientific Indexing(ISI) calculate based on International Citation Report(ICR)




Mammogram Image Segmentation by Watershed Algorithm and Classification through k-NN Classifier

B.N. Beena Ullala Mata and Dr.M. Meenakshi


This paper presents a novel approach to detect the tumors in the mammogram images based on watershed algorithm. To increase the performance of the classifier, watershed algorithm combined with K-NN classifier is implemented. The gray level co-occurrence matrices (GLCM?S) are obtained from the mammogram images, through the extraction of Halarick?s texture features are classified. American Society of cancer, UK, provides the benchmark data, MIAS (Mammographic Image Analysis Society) database for the validation of proposed algorithm. These images are used for further analysis by classification into three categories using the algorithms. Mammogram abnormalities are found to be detected using the proposed algorithm with the available ground truth given in the data base (mini-MIAS database), the accuracy obtained is as high as 83.33%.

Keywords: Halaricks Texture Features, k-NN, MIAS.

Volume: 8 | Issue: 1

Pages: 01-07

Issue Date: January , 2018

DOI: 10.9756/BIJAIP.8352

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