AUTOMATIC DETECTION OF BREAST CANCER MASS IN MAMMOGRAMS USING MORPHOLOGICAL OPERATORS AND FUZZY C -MEANS CLUSTERING
S. Saheb Basha, K. Satya Prasad · 2009
Breast cancer is one of the major causes of death among women. Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. Primary prevention seems impossible since the causes of this disease is still remain unknown. An improvement of early diagnostic techniques is critical for women’s quality of life. Mammography is the main test used for screening and early diagnosis. Early detection performed on X-ray mammography is the key to improve breast cancer prognosis. In order to increase radiologists diagnostic performance, several computer-aided diagnosis (CAD) schemes have been developed to improve the detection of primary signatures of this disease: masses and micro calcifications. Masses are space-occupying lesions, described by their shapes, margins, and denseness properties. A benign neoplasm is smoothly marginated, whereas a malignancy is characterized by an indistinct border that becomes more speculated with time. Because of the slight differences in X-ray attenuation between masses and benign glandular tissue, they appear with low contrast and often very blurred. Micro calcifications are tiny deposits of calcium that appear as small bright spots in the mammogram. This paper presents a research on mammography images using Morphological operators and Fuzzy c – means clustering for cancer tumor mass segmentation. The first step of the cancer signs detection should be a segmentation procedure able to distinguish masses and micro calcifications from background tissue using Morphological operators and finally fuzzy c- means clustering (FCM) algorithm has been implemented for intensity – based segmentation. The proposed technique shows better results