Efficient Segmentation and Classification of Mammogram Images with Fuzzy Filtering

Background: Worldwide and across India breast cancer is the most common cause of cancer deaths in women. Early or timely detection leads to decrease in the mortality rate. Hence, classification of patients based on the size of the tumor/abnormal masses and less treatment cost must be high priority. Methods/Statistical Analysis: In this paper mammogram images are being acquired from real time and standard databases for imaging the suspected patients. The main purposes of the suggested methods are to diagnose the cancer using fuzzy rules with minimum phases in implementation. Important factors were drawn from the images for subsequent investigation and analysis with the help of Fuzzy Enhanced Mammogram Segmentation scheme. The paper presents two methods and is implemented in (i.e. FEM1 and FEM2) Mat lab programming environment. Results: The images examined were marked by qualified Radiologist and extracted the images using Photoshop tool. The proposed methodologies were evaluated for real images and Mammographic Image Analysis Society (MIAS) database images consists of 320 images for 160 patients each of 1024x1024 resolutions based gray level images. Based on the results it is found that the CDR for FEM1 is 87% whereas FEM2 demonstrates only 77% and also takes 6.25 times lesser execution time. Radiologists need more precise and reduced processing time making the outcome of FEM1 method more practicable. For the evaluation of performance, statistical properties like Similarity Index (SI), Correct Detection Ratio (CDR), and Under Segmentation Error (USE) are computed. The paper presents computations of segmentation efficiency, enhancement performance and comparative analysis between the method 1 and method 2 in terms of segmentation efficiency and CPU processing time. Finally Support Vector Method is used to classify whether the mammogram under test is normal or abnormal. Conclusion: FEM1 outperforms other similar methods. The proposed work provides faster, accurate results and more useful for the diagnosis and classify the abnormal tumors or masses at a cheaper cost.