Lesion Detection using Segmentation and Classification of Mammograms

L. Vibha, G.M. Harshavardhan, K. Pranaw, P. Deepa Shenoy, K.R. Venugopal, and L.M. Patnaik (India)


Digital mammography, Watershed Algorithm, Segmentation, Random forest, classification.


The national cancer institute (NCI) estimates that 1 in 8 women in USA (12.6%) develop breast cancer, during their life time. Mammography is a medical imaging technique that combines, low-dose radiation and high contrast, high-resolution film for examination of the breast and screening for breast cancer. The aim of a screening mammogram is to detect a tumor that cannot be physically detected. This paper proposes a method for detection of tumor using Watershed Algorithm, and further classifies it as benign or malignant using Watershed Decision Classifier (WDC). The technique consists of four phases namely, the pre-processing of image, detecting the presence of tumor, creation of the data base and finally classifying and categorizing them into two groups such as Cancerous or Benign. Experimental results show that this method performs well with the classification accuracy reaching nearly 88.38%.

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