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This paper presents a method for segment and detects the boundary of different breast tissue regions in mammograms by using dynamic K-means clustering algorithm and Seed Based Region Growing (SBRG) techniques. Firstly, the K-means clustering is applied for dynamically and automatically generated the seeds points and determines the thresholds' values for each region. Secondly, the region growing algorithm is used with previously generated input parameters to divide mammogram into homogeneous regions according to the intensity of the pixel. The main goal of this method is to automatically segment and detect the boundary of different disjoint breast tissue regions in image mammography. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and qualitative and quantitative evaluation of density changes. So, using a computer-aided detection/diagnosis (CAD/CADx) system as supplement to the radiologists' assessment has an important role. In order to evaluate our proposed method we used the wellknown Mammographic Image Analysis Society (MIAS) database. The obtained qualitative and quantitative results demonstrate the efficiency of this method and confirm the possibility of its use in improving the computer-aided detection/diagnosis.

Abstract-Breast cancer is the most common cancer and the leading cause of morbidity and mortality among women's age between 50 and 74 years across the worldwide. In this paper we've proposed a method to detect the suspicious lesions in mammograms, extracting their features and classify them as Normal or Abnormal and Benign or Malignant for diagnosing of breast cancer. This method consists of two major parts: The first one is detection of regions of interest (ROIs). The second one is diagnosing of detected ROIs. This method was tested by Mini Mammography Image Analysis Society (Mini-MIAS) database. To check method's performance, we've used FROC (Free-Receiver Operating Characteristics) curve in the detection part and ROC (Receiver Operating Characteristics) curve in the diagnosis part. Obtained results show that the performance of detection part has sensitivity of 94.27% at 0.67 false positive per image. The performance of diagnosis part has 94.29% accuracy, with 94.11% sensitivity, 94.44% specificity in the classification as normal or abnormal mammogram, and has achieved 94.4% accuracy, with 96.15% sensitivity and 94.54% specificity in the classification as Benign or Malignant mammogram.

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