Overview Of Current Biomedical Image Segmentation Methods

Keywords

Artificial intelligence techniques; medical image segmentation

Abstract

Medical image processing is a very active and fast-growing field that has evolved into an established discipline. Accurate segmentation of medical images is a fundamental step in clinical studies for diagnosis, monitoring, and treatment planning. Manual segmentation of medical images is a time consuming and a tedious task. Therefore the automated segmentation algorithms with high accuracy are of interest. There are several critical factors that determine the performance of a segmentation algorithm. Examples are: the area of application of segmentation technique, reproducibility of the method, accuracy of the results, etc. The purpose of this review is to provide an overview of current image segmentation methods. Their relative efficiency, advantages, and the problems they encounter are discussed. In order to evaluate the segmentation results, some popular benchmark measurements are presented.

Publication Date

3-17-2017

Publication Title

Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

Number of Pages

803-808

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CSCI.2016.0156

Socpus ID

85017324748 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85017324748

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