By Nagaraj P.Y, Central University of Karnataka

Abstract 

Medical image segmentation is a challenging task that poses a number of challenges. In medical imaging, many techniques produce poor contrast and inhomogeneous appearances, resulting in over- and under- segmentation. The dermoscopic images of skin lesions often show large variations in size and shape, making the construction of prior shape models challenging. A panoramic X-ray image is also challenged by many flaws, including the variation in tooth size between patients and the spacing between missing teeth. The human brain can be separated into different regions based on the type of matter it contains, including gray matter, white matter, and cerebrospinal fluid. An MRI segmentation involves dividing the image into well-defined regions, in which pixels have similar intensities and textures. It is difficult to segment brain tissues from MRI images due to their tissue intensities, and partial volume effects. To overcome these problems, we propose CNN architectures for segmenting medical images.