Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.
Correlation detector (CD) has been widely used to determine whether a legitimate watermark exists in a suspected watermarked object. The watermarked and unwatermarked images are perceived as positive and negative class respectively. Hence, Support Vector Machine (SVM) is used as the classifier of water-marked and unwatermarked digital image due to its ability of separating both linearly and non-linearly separable data. Hyperplanes of various detectors are briefly elaborated to show how SVM's hyperplane is suitable for Stirmark attacked watermarked image. Cox's spread spectrum watermarking scheme is used to embed the watermark into digital images. Then, SVM is trained with both the watermarked and unwatermarked images. Receiver Operating Characteristics graphs are plotted to assess the false positive and false negative probability of both the CD and SVM classifier. Both watermarked and unwatermarked images are later attacked under Stirmark, and then tested on the CD and SVM classifier. The preprocessing and optimal parameters setting enable the trained SVM to achieve substantially better results than those resulting from the CD.
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