Segmentation of Medical Images

Abstract

The use of digital images has increased at a rapid pace over the past decade. Many recent imaging modalities in medicine, such as magnetic resonance imaging (MRI), computed tomography (CT), digital mammography also generate images directly in digital form. These technologies have greatly increased knowledge of normal and diseased anatomy for medical research and are a critical component in diagnosis and treatment planning. With the increasing size and number of medical images, the use of computers in facilitating their processing and analysis has become necessary. Estimation of the volume of the whole organ, parts of the organ and/or objects within an organ i.e. tumors is clinically important in the analysis of medical image. The relative change in size, shape and the spatial relationships between anatomical structures obtained from intensity distributions provide important information in clinical diagnosis for monitoring disease progression for the radiologist. In particular, computer algorithms for the delineation of anatomical structures and other regions of interest are a key component in assisting and automating specific radiological tasks. These algorithms, called image segmentation algorithms, play a vital role in numerous biomedical imaging applications. There is no single algorithm which gives the best results for segmentation of every medical image.