Automated Abnormal Mass Detection in the Mammogram Images Using Chebyshev Moments

Document Type

Article

Journal/Book Title/Conference

Research Journal of Applied Sciences, Engineering and Technology

Volume

5

Issue

2

Publisher

Maxwell Science Publication

Publication Date

6-23-2013

First Page

513

Last Page

518

Abstract

Breast cancer is the second leading cause of cancer mortality among women after lung cancer. Early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease. In this study a new system for automated mass detection in mammography images is presented as being more accurate and valid. After optimization of the image and extracting a better picture of the breast tissue from the image and applying log-polar transformation, Chebyshev moments can be calculated in all areas of breast tissue. Then after extracting effective features in the diagnosis of mammography images, abnormal masses, which are important for the physician and specialists, can be determined with applying the appropriate threshold. To check the system performance, images in the MIAS (Mammographic Image Analysis Society) mammogram database have been used and the results allowed us to draw a FROC (Free Response Receiver Operating Characteristic) curve. When compared the FROC curve with similar systems experts, the high ability of our system was confirmed. In this system, images of different thresholds, specifically 445, 450, 455 are processed and then put through a sensitivity analysis. The process garnered good results 100, 92 and 84%, respectively and a false positive rate per image 2.56, 0.86, 0.26, respectively have been calculated. Comparing other automatic mass detection systems, the proposed method has a few advantages over prior systems: Our process allows us to determine the amount of false positives and/or sensitivity parameters within the system. This can be determined by the importance of the detection work being done. The proposed system achieves 100% sensitivity and 2.56 false positive for every image.

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