scholarly journals An Applied Research on Improved Watershed Algorithm in Medical Image Segmentation

2016 ◽  
Vol 9 (11) ◽  
pp. 191-198
Author(s):  
BenZhai Hai ◽  
RuiYun Xie ◽  
PeiYan Yuan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Applied Research ◽  
Medical Image Segmentation ◽  
Watershed Algorithm

Watershed Algorithm for Medical Image Segmentation Based on Morphology and Total Variation Model

2019 ◽  
Vol 33 (05) ◽  
pp. 1954019 ◽  
Author(s):  
Yingbo Liang ◽  
Jian Fu
Keyword(s):  
Image Segmentation ◽  
Total Variation ◽  
Medical Image ◽  
Brain Lesion ◽  
Medical Image Segmentation ◽  
Brain Lesions ◽  
Variational Model ◽  
Watershed Algorithm ◽  
Total Variation Model ◽  
Variation Model

The traditional watershed algorithm has the limitation of false mark in medical image segmentation, which causes over-segmentation and images to be contaminated by noise possibly during acquisition. In this study, we proposed an improved watershed segmentation algorithm based on morphological processing and total variation model (TV) for medical image segmentation. First of all, morphological gradient preprocessing is performed on MRI images of brain lesions. Secondly, the gradient images are denoised by the all-variational model. While retaining the edge information of MRI images of brain lesions, the image noise is reduced. And then, the internal and external markers are obtained by forced minimum technique, and the gradient amplitude images are corrected by using these markers. Finally, the modified gradient image is subjected to watershed transformation. The experiment of segmentation and simulation of brain lesion MRI image is carried out on MATLAB. And the segmentation results are compared with other watershed algrothims. The experimental results demonstrate that our method obtains the least number of regions, which can extract MRI images of brain lesions effectively. In addition, this method can inhibit over-segmentation, improving the segmentation results of lesions in MRI images of brain lesions.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation
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