Region Feature Extraction of Brain CT Image for Classification

2008 ◽  
Author(s):  
Fei Peng ◽  
Kehong Yuan ◽  
Shu Feng ◽  
Wufan Chen
Keyword(s):  
Feature Extraction ◽  
Ct Image ◽  
Brain Ct ◽  
Region Feature

Attention-based Full Slice Brain CT Image Diagnosis with Explanations

2021 ◽  
Author(s):  
Guanghui Fu ◽  
Jianqiang Li ◽  
Ruiqian Wang ◽  
Yue Ma ◽  
Yueda Chen
Keyword(s):  
Ct Image ◽  
Brain Ct ◽  
Image Diagnosis

scholarly journals Study on threshold segmentation of multi-resolution 3D human brain CT image

2018 ◽  
Vol 11 (06) ◽  
pp. 1850037
Author(s):  
Ling-ling Cui ◽  
Hui Zhang
Keyword(s):  
Human Brain ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Pathological Diagnosis ◽  
Ct Image ◽  
Segmentation Method ◽  
Signal To Noise ◽  
Threshold Segmentation ◽  
Edge Pixel ◽  
Brain Ct

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Segmentation of Brain CT Image Machine Learning

1997 ◽  
Vol 3 (2) ◽  
pp. 193
Author(s):  
Ho Sung Kim ◽  
Young Ran Lee
Keyword(s):  
Machine Learning ◽  
Ct Image ◽  
Brain Ct

scholarly journals Brain CT image segmentation based on 3D slicer

2020 ◽  
Vol 3 (1) ◽  
pp. 34-42
Author(s):  
Jincai Chang ◽  
Jianzhong Cui ◽  
Yuxuan Wang ◽  
Han Wang ◽  
Keqin Shen
Keyword(s):  
Image Segmentation ◽  
Ct Image ◽  
3D Slicer ◽  
Brain Ct
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