A New Parallel Method for Medical Image Segmentation Using Watershed Algorithm and an Improved Gradient Vector Flow

2018 ◽  
pp. 641-651
Author(s):  
Hayat Meddeber ◽  
Belabbas Yagoubi
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Watershed Algorithm ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Parallel Method

Sigmoid gradient vector flow for medical image segmentation

2012 ◽  
Author(s):  
Yuhua Yao ◽  
Lixiong Liu ◽  
Lejian Liao ◽  
Ming Wei ◽  
Jianping Guo ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector

scholarly journals Medical Image Segmentation based on Fully Convolutional Network and Minimizing Energy Between Curves

TEM Journal ◽  
2020 ◽  
pp. 1348-1356
Author(s):  
Vo Thi Hong Tuyet ◽  
Nguyen Thanh Binh
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Contour Detection ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
High Quality ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Object Contour

Energy between curves of image has useful for object contour. The edge map is an important task for recognition. The shape that is found by linking between edges will clearly present the useful information of objects. The aim of medical image segmentation is the representation of a medical image into small pieces. In this process, feature extraction must adapt with edge map completely. This paper proposed a solution for medical image segmentation based on fully convolutional network with gradient vector flow snake in bandelet domain. Our approach depends on decomposition in bandelet domain and reconstruction in contour detection by fully convolutional network combining with gradient vector flow snake. To improve the accuracy of the feature's extraction processing, the proposed method detected the edge map in bandelet domain by using fully convolutional network. And its reconstructed objects contour by using gradient vector flow snake combined with the boundary condition. The results of the proposed method have the segmentation clearly with small details of medical images in high-quality and low-quality cases.

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