Multiple Attribute Feature Extraction and High Support Vector Classifier for Identification of Cytomegalovirus Images

Digital image processing is a broad area consists of different concepts to emphasize the quality of the images. The digital image processing has been engaged in various areas such as remote sensing, video processing, image sharpening, colour, pattern recognition, image compression etc. The digital image processing plays an immeasurable role in the field of biomedical. The clarity of the images are important for any types of medical diagnosis leads with human body. In this paper the image of cytomegalovirus is taken and processed to show the improvement of the affected images. Though there are many facilities to identify any type of diagnosis, detection of viruses in terms of images also plays a vital role. The cytomegalovirus affects almost all ages of people, but it shows more impact on pregnant women, if the women gets infected the foetus also gets affected so this leads to congenital infections of the baby such as impair vision, autism, hearing loss, birthmarks etc. Once the person gets affected it retains for lifetime. In this proposed work the input of the image is taken and processed with ROI and MAFE Algorithm with PCA reduction and HSVM is used for classification and Identification of the image is done.