Automatic ultrasound image segmentation based on local entropy and active contour model. (1st August 2019)

• Record Type: Journal Article
• Authors: Zong, Jing-jing; Qiu, Tian-shuang; Li, Wei-dong; Guo, Dong-mei
• Is Part Of: Computers & mathematics with applications. Volume 78:issue 3(2019)
• Page Start: 929
• Page End: 943
• Publication Date: 2019-08-01
• Subjects: Automatic ultrasound segmentation -- Active contour model -- Partial differential equations -- Level set method -- Local entropy; Electronic data processing -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.28541
• DOI: 10.1016/j.camwa.2019.03.022
• Languages: English
• ISSNs: 0898-1221

Abstract

Abstract: It is still a challenging task to segment ultrasound medical images automatically and accurately because of the poor quality of images. To address these problems, a two-stage automatic segmentation scheme based on local entropy and a proposed active contour model for ultrasound images are put forward in this paper. First, a new region-based active contour model in the level set formulation, driven by global and local intensity information, is established for the segmentation. Furthermore, for automatically segmenting the ultrasound images, a coarse segmentation is performed utilizing local entropy information of the ultrasound images, then the coarse segmentation result is used as the initial value of the explicit segmentation based on the proposed model. Several experiments on real ultrasound images demonstrate that the proposed method outperforms other methods on both visual perception and objective evaluation metrics.