Open AccessACTIVE CONTOUR MODELS-BASED SEGMENTATION OF LEFT VENTRICLE IN ULTRASOUND IMAGES FOR DIFFERENT AXES VIEWS
Author(s) -
Yang Zheng,
Zhongping Chen,
Jiake Wang,
Shu Jiang,
Yu Liu
Publication year - 2021
Publication title -
journal of mechanics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 30
eISSN - 1793-6810
pISSN - 0219-5194
DOI - 10.1142/s0219519421500317
Subject(s) - hausdorff distance , segmentation , active contour model , initialization , ventricle , contouring , artificial intelligence , constraint (computer aided design) , computer vision , level set (data structures) , computer science , active shape model , image segmentation , mathematics , geometry , medicine , computer graphics (images) , cardiology , programming language
Segmentation of the left ventricle in ultrasound images for viewing through different axes is a critical aspect. This paper proposes the development of novel active contour models with shape constraint to segment the left ventricle in three different axis views of the ultrasound images. The shapes observed in all the axis views of the left ventricle were not similar. According to the cardiac cycle, the valve opening in the end-diastolic phase influenced the left ventricle segmentation; hence, a shape constraint was embedded in the active contour model to keep ventricle’s shape, especially in the Apical long-axis view and Apical four-chamber view. Furthermore, for different axes views, diverse active contour models were proposed to fit each situation. The shape constraint in each function for different views exhibited a specific shape during the function iteration. In order to speed up the algorithm evolution, previous results were used for the initialization of the present active contour. We evaluated the proposed method on 57 patients with three different views: Apical long-axis view, Apical four-chamber view and Short-axis view at the papillary muscle level and obtained the Dice similarity coefficients of [Formula: see text], [Formula: see text] and [Formula: see text] and the Hausdorff distance metrics of [Formula: see text], [Formula: see text] and [Formula: see text], respectively. The qualitative and quantitative evaluations showed an advantage of our method in terms of segmentation accuracy.