Open AccessSequential shape similarity for active contour based left ventricle segmentation in cardiac cine MR image
Author(s) -
Ke Bi,
AUTHOR_ID,
Yue Tan,
Cheng Ke,
Qingfang Chen,
Yuanquan Wang,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022074
Subject(s) - hausdorff distance , artificial intelligence , active contour model , similarity (geometry) , segmentation , computer vision , pattern recognition (psychology) , active shape model , metric (unit) , sequence (biology) , image segmentation , computer science , vector flow , constraint (computer aided design) , mathematics , image (mathematics) , geometry , engineering , operations management , biology , genetics
Delineation of the boundaries of the Left Ventricle (LV) in cardiac Magnetic Resonance Images (MRI) is a hot topic due to its important diagnostic power. In this paper, an approach is proposed to extract the LV in a sequence of MR images. In the proposed paper, all images in the sequence are segmented simultaneously and the shape of the LV in each image is supposed to be similar to that of the LV in nearby images in the sequence. We coined the novel shape similarity constraint, and it is called sequential shape similarity (SSS in short). The proposed segmentation method takes the Active Contour Model as the base model and our previously proposed Gradient Vector Convolution (GVC) external force is also adopted. With the SSS constraint, the snake contour can accurately delineate the LV boundaries. We evaluate our method on two cardiac MRI datasets and the Mean Absolute Distance (MAD) metric and the Hausdorff Distance (HD) metric demonstrate that the proposed approach has good performance on segmenting the boundaries of the LV.