Recent Survey on Medical Image Segmentation

Abstract

This chapter presents a survey on the techniques of medical image segmentation. Image segmentation methods are given in three groups based on image features used by the method. The advantages and disadvantages of the existing methods are evaluated, and the motivations to develop new techniques with respect to the addressed problems are given. Digital images and digital videos are pictures and films, respectively, which have been converted into a computer-readable binary format consisting of logical zeros and ones. An image is a still picture that does not change in time, whereas a video evolves in time and generally contains moving and/or changing objects. An important feature of digital images is that they are multidimensional signals, i.e., they are functions of more than a single variable. In the classical study of the digital signal processing the signals are usually one-dimensional functions of time. Images however, are functions of two, and perhaps three space dimensions in case of colored images, whereas a digital video as a function includes a third (or fourth) time dimension as well. A consequence of this is that digital image processing, meaning that significant computational and storage resources are required.

A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images

“…Extensive studies have been conducted for segmenting the medical image. Fuzzy set theory and information theory have a huge impact on image segmentation [1][2][3]. Fuzzy entropy has become one of the important research points for threshold based medical image segmentation.…”