Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster

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.

SaTransformer: Semantic‐aware transformer for breast cancer classification and segmentation

“…These above phenomena have a serious impact on the effectiveness of treatment planning. Therefore, automatic delect, and accurate breast cancer segmenting and classifying are highly desirable in clinical practice [3][4][5]. However, accurately segmenting and classifying breast cancer still meet many challenges as shown in Figure 1.…”

Directionally weakened diffusion for image segmentation using active contours

“…The region-based active contours always adopt an implicit representation under the level-set framework, and the edgebased models usually take an explicit parametric form. Recently, the deep learning-based approaches have gained popularity in image segmentation (34-37); however, the deep learning methods need a large number of training examples, and the active contours are still an active topic in the computer vision community; for example, the studies in (4,14,(20)(21)(22)30). In addition, we refer the interested readers to Antonelli et al (38) for the review of the computational models for image segmentation.…”