OBJECTIVE: Breast cancer represents themost frequently diagnosed cancer in women. In order to reduce mortality, early detection of breast cancer is important, because diagnosis is more likely to be successful in the early stages of the disease. On the average, the reader's sensitivity can be increased by 10%with the assistance of computer-aided diagnosis (CAD) system. This paper presents a CAD system for the automatic detection of clustered micro-calcifications in digitized mammograms. METHODS: The proposed system consists of three main steps. First, breast region is segmented from original mammogram using contrast property of grey level co-occurrence matrix(GLCM). Second, potential micro-calcification pixels in the mammograms are detected by foveal method. Third, in order to reduce false-positive rate, individual micro-calcifications are detected by a set of 8 features extracted from the potential individual micro-calcification objects. RESULTS: In the result, Specificity and sensitivity are used to evaluate the detection performance of micro-calcifications.(sensitivity : 93.1%, specificity : 87.5%). CONCLUSION: This study could be a useful method for diagnosis of breast cancer as a CAD system.
Breast ; Breast Neoplasms ; Diagnosis ; Female ; Humans ; Mortality ; Sensitivity and Specificity

OBJECTIVE: The goal of this study was to develop a novel pupil and iris segmentation algorithm. We evaluated segmentation performance based on a fractal model. Two methods were compared: Daugman's and our new proposed method. METHODS: We received 200 anterior segment images with 3,872x2,592 pixels. Here we present an active contour model that accurately detects pupil boundaries in order to improve the performance of segmentation systems. We propose a method that uses iris segmentation based on a fractal model. We compared the performance of Daugman's method and the proposed new method and statistically analyzed the results. RESULTS: We manually compared segmentation with the Daugman's method and the new proposed method. The findings showed that the proposed segmentation accuracy was about 2.5 percent higher than Daugman's method. There was a significant difference (p<0.05) between the under and over data between the two methods. CONCLUSION: The results of this study show that the new proposed method was more accurate than the conventional method for the measurement of segmentation of the eye by CAD (Computer-aided Diagnosis).
Eye ; Fractals ; Iris ; Pupil