M‐WRSF model for medical image segmentation

Abstract

In this Letter, multichannel weighted region-scalable fitting segmentation model (M-WRSF) is proposed for medical image segmentation. The authors have utilised a new edge detection function to improve the performance of image segmentation approaches that already exists on weak edges and the grey-scale inhomogeneity of some medical images. The M-WRSF model introduces a novel punishment item to improve numerical stability and augments time interval to boost iterative efficiency. The Gaussian kernel function is added on the basis of original model to enhance robustness. On the other side, the original medical images need to make de-noising operation before the multichannel active contour model segment them, and also level-set initial curve has no effect on the segmentation results. The benefits of the new M-WRSF model, specifically, the increased efficiency and usability, are verified in the simulation experiment.

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

“…In [ 79 ], the authors propose an image segmentation method based on a scalable multichannel weighted region model. In order to improve the performance of image Mosaic, a new edge detection function is proposed.…”

A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches

“…In [281], a multi-channel weighted region scalable fitting (M-WRSF) segmentation model for medical image segmentation is proposed. In this M-WRSF model, a new penalty term is introduced to improve the numerical stability and the time interval is increased to improve the iteration efficiency.…”