Breast cancer is considered to be one of the leading causes of deaths among females in United States. All over the global level approximately 10000 women are diagnosed with this disease per year and approximately 3500 of these women are die from this types of cancer. In this paper, we propose a Complex Event Processing (CEP) Engine based on Support Vector Machine. Currently the Effective method for early detection and screening of Breast Cancer is Mammography Techniques. The detection of Tumor method follows the scheme of a) Mammogram image preprocessing b) The Segmentation Tumor area c) The Extraction of features and the use of support Vector Machine classification method.The monogram enhancement and segmentation techniques play an important role to improve the detection and diagnosis of breast cancer. The results reveal that the application of Event processing techniques improves the classification of images in medical domain and produces accurate results in order to help radiologist assessment.

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Personal identification from the iris images acquired under less-constrained imaging environment is highly challenging. Such environment requires the development of efficient iris segmentation approach and recognition strategy which can exploit multiple features available for the potential identification. So, along with the iris features periocular features have increasing attention in biometrics technology. For the recognition purpose iris and periocular information are collected from both the eyes of same person simultaneously. The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of image for periocular biometric is expected to require less subject cooperation. In this chapter, a dual iris based multimodal biometric system that increases the performance and accuracy of the typical iris recognition system is proposed.