Iterative threshold segmentation method based on information entropy

2021 ◽  
Author(s):  
Jiwen Cui ◽  
Dongyu Guo ◽  
Zhe Li
Keyword(s):  
Information Entropy ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Iterative Threshold

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

Research of Image Segmentation Based on Iterative Threshold

2014 ◽  
Vol 701-702 ◽  
pp. 330-333
Author(s):  
Lei Shao ◽  
Yi Mu ◽  
Peng Guo ◽  
Jun Liu ◽  
Guo Ling Dong ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Image Recognition ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Target Character ◽  
The One ◽  
Definition Of ◽  
Iterative Threshold

Image segmentation is the key step in image recognition,the result of segmentation affects the one of recognition directly.The article introduces the concept and detailed definition of the image segmentation. The segmentation algorithm of iterative threshold in detail. According to the intrinsic characteristics of weed images, just can use the iteration threshold segmentation method, and implements them by Matlab programme, then processes three weed images, respectively to obtain effective results , and establishes a good base for the pick-up of the target character.

Study of Segmentation Threshold Based on Wavelet Transform

2013 ◽  
Vol 756-759 ◽  
pp. 3855-3859
Author(s):  
Jian Yi Li ◽  
Hui Juan Wang
Keyword(s):  
Wavelet Transform ◽  
Digital Image ◽  
Region Growing ◽  
Detection Methods ◽  
Mathematical Tool ◽  
Matlab Simulation ◽  
Segmentation Method ◽  
Transform Method ◽  
Threshold Segmentation ◽  
Digital Image Enhancement

Based on the research of the four kinds of algorithms of digital image segmentation, based on edge detection methods, based on region growing method, threshold segmentation method and digital image threshold segmentation method based on wavelet transform, using MATLAB simulation of all digital image enhancement and segmentation process, the obtained results are analyzed, proving the threshold segmentation wavelet transform method has unparalleled advantages in information extraction in medical image. Wavelet transform is a mathematical tool widely used in recent years, compared with the Fu Liye transform, the window of Fu Liye transform, wavelet transform is the local transform of space and frequency, it can be very effective in extracting information from the signal [[1.

Local Threshold Segmentation Method Based on Multi-Direction Grayscale Wave for Image

2020 ◽  
Vol 57 (6) ◽  
pp. 061016
Author(s):  
吴正平 Wu Zhengping ◽  
马占稳 Ma Zhanwen ◽  
颜华 Yan Hua ◽  
张兆蒙 Zhang Zhaomeng ◽  
尹凡 Yin Fan
Keyword(s):  
Segmentation Method ◽  
Threshold Segmentation ◽  
Local Threshold

Design of Threshold Segmentation Method for Quantum Image

2019 ◽  
Vol 59 (2) ◽  
pp. 514-538 ◽  
Author(s):  
Panchi Li ◽  
Tong Shi ◽  
Ya Zhao ◽  
Aiping Lu
Keyword(s):  
Segmentation Method ◽  
Threshold Segmentation ◽  
Quantum Image

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.

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