Segmentation Methods for Biomedical Images
Roberto Rodríguez · InTech eBooks · 2011
Nowadays biomedical imaging provides major aid in many branches of medicine; it enables and facilitates the capture, transmission and analysis of biomedical images as well as providing assistance towards medical diagnoses. Medical imaging is still on the rise with new imaging modalities being added and continuous improvements of devices’ capabilities. Segmentation and contour extraction are important steps towards image analysis. Segmented images are now used routinely in a multitude of different applications, such as, diagnosis, treatment planning, localization of pathology, study of anatomical structure, computer-integrated surgery, among others. However, image segmentation remains a difficult task due to both the variability of object shapes and the variation in image quality. Particularly, biomedical images are often corrupted by noise and sampling artifacts, which can cause considerable difficulties when applying rigid methods. In many occasions, segmentation is considered as a process of classification of objects in a scene, and also in certain measure, it is equivalent to its recognition (objects) since, as a consequence of segmentation the different objects (physical realizations of classes or abstract patterns), these stay perfectly located inside the digital image. In a graphic way in Figure 1, it is represented the segmentation process of a simple image. It is appreciated in this Figure that as result of segmentation the image was transformed from a gross digital image with all their information in form of gray-levels to an image much more simplified, where the different objects are well distinguished.