Medical image analysis of image segmentation and registration techniques

Abstract

Medical Image Analysis is essential in order to detect and diagnose the various types of Cancers. In recent years there is a rise in the death rate of patients suffering from brain cancer and lung cancer. The chances of survival among people can increase if the detection is done in the earlier stage. The widely used diagnose technique is Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Images which are used to present the cancer location in the brain and lungs. In this work the brain tumour and lung cancer is detected and registered through the medical images in three stages. First is the pre -processing stage, a set of medical images is filtered for removing noise by Gaussian filter, Secondly the image is segmented automatically using Otsu and KNN clustering. Edge Detection method is done by using canny detection method. Third stage is the feature extraction stage, in this stage the segmented MRI and CT images are registered to obtain the tumour. The feature detection methods used are the SIFT and SURF algorithm for both brain and lung images to obtain effective results. The SIFT and Affine Transform registration technique used increases the speed and reduces the complexity of geometrical alignments of two images that is the reference and sensed images. It also displays the minute difference between two identical images rapidly and accurately which is essential for medical diagnoses.