An Efficient Image Segmentation and Classification of Tumors in Mammography Image using K-Means Clustering with ROI and ANN Classifier

Abstract

Breast cancer is commonly occurred in women that causes of death. An early detection of breast cancer can be used to long survival of patients. The tumor is detected in breast using mammography X-ray image. This mammography is one of the special cases of CT scan that is utilized to discover the breast cancer. It is screening method to find the malignant tumor cells in breast among women at early stage to avoid the deaths of patient. However, the radiologists not forever provide accurate results and detection of malignant tumor in breast is a one of the challenging issue due to the tumor cells structure. Therefore, this paper presents K-means clustering with Probabilistic neural networks for improvement of mammogram image quality to provide accurate results. K- Means clustering is applied to segment the mammogram image efficiently and presents an automatic support system for image classification using trained probabilistic neural networks and Artificial Neural networks (ANN) classification will be employed to classify the stage of breast cancer tumor that is caring, malignant or standard. Describing the ROI (Region of Interest), utilizing Region growing segmentation technique, evaluating the area of assumed cancer and establish the image classification of the area on the mammogram image using Sample K-Means Clustering and PNN techniques. Gray Level Co-occurrence Matrix (GLCM) is to extract the features from the texture that features may be in the form of structure, location and outside etc. In this process, the preprocessing method is applied to reduce the noise from the mammogram image for the image enhancement. For the preprocessing, the filter methods are used to give image quality. The performance of k-means clustering and PNN techniques provides fast and accurate image segmentation and classification than the other techniques to detect tumor cells in breast at early stage efficiently.