Local Independent Projection Based Classification Using Fuzzy Clustering

In Medical diagnosis, through Magnetic Resonance Images Robustness and accuracy of the Prediction algorithms are very important, because the result is crucial for treatment of Patients. There are many popular classification and clustering algorithms used for predicting the diseases from Images. The goal of clustering a medical image is to simplify the representation of an image into a meaningful image and make it easier to analyze. Several Clustering and Classification algorithms are aimed at enhancing the Prediction accuracy of diagnosis Process in detecting1 abnormalities such as Cancer and white matter lesions from MR Images. Tumor is an uncontrolled growth of tissues in any part of the body. Image mining facilitates the extraction of hidden information, image data association, or other patterns not clearly accumulated in the images. Image mining is an interdisciplinary effort that provides significant application in the domain of machine learning, image processing, image retrieval, data mining, database, computer vision, and artificial intelligence. Even though there exists growth of several applications and techniques in the individual research domain mentioned above, research in image mining has to be explored and investigated their existing research problems in image mining, modern growth in image mining, predominantly, image mining frameworks, modern techniques and systems. Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. The American Brain Tumor Association estimates that about 40,900 people will be diagnosed with a primary brain tumor (rate of 14 percentage of 100,000 people) and 12,900 peoples die due to brain tumor. The segmentation technique helps medical image solutions in higher frequency and it has more impact on medical image processing. The image segmentation process has been used in variety of medical problems from identifying born crush to brain tumors. All MR images are affected by random noise. The noise comes from the stray currents in the detector coil due to the fluctuating magnetic fields arising from random ionic currents in the body, or the thermal fluctuations in the detector coil itself. When the level of noise is significant in an MR image, tissues that are similar in contrast could not be delineated effectively, causing errors in tissue segmentation.