Open AccessActive contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster
Author(s) -
Feng Bao,
Zhou Haoyang,
Feng Jin,
Chen Yehang,
Liu Yu,
Yu Tianyou,
Liu Zhuangsheng,
Long Wansheng
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12530
Subject(s) - active contour model , artificial intelligence , segmentation , jaccard index , computer science , pattern recognition (psychology) , hausdorff distance , image segmentation , sørensen–dice coefficient , breast mri , feature (linguistics) , computer vision , boundary (topology) , similarity (geometry) , magnetic resonance imaging , breast cancer , mathematics , mammography , cancer , image (mathematics) , medicine , radiology , mathematical analysis , linguistics , philosophy
Abstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE‐MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time‐domain features to enhance the difference between the lesions and other tissues. Second, an edge‐stop function (ESF) is introduced by combining the image intensity, time‐domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models.