Application of Genetic Algorithm to Image Segmentation

2014 ◽  
Vol 685 ◽  
pp. 642-645
Author(s):  
Hai Yan Liu
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Related Area ◽  
Selection Of

Image segmentation is very important in image analysis that needs to separate the related area for the general target distinguishing and analyzing an image, and it can make further use of the target, such as characteristic pick-up and measure on the basis of image processing disposal. In this dissertation, image segmentation based on genetic algorithm will be described. The selection of threshold and the process about image segmentation based on genetic algorithm are described. Finally, image segmentation based on genetic algorithm is used on a picture by Matlab, the result can be accepted. Therefore, it is significant to make analysis on image segmentation based on genetic algorithm.

Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 925-928 ◽  
Author(s):  
Zhi Bo Xu ◽  
Pei Jiang Chen ◽  
Shi Li Yan ◽  
Tai Hua Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Image Process ◽  
Segmentation Method ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Maximum Variance ◽  
Segmentation Image ◽  
Selection Of

Threshold segmentation method was widely applied in image process and the selection of threshold affected the final results of image segmentation to a large extent. In order to improve the accuracy and the calculation speed of image segmentation, an Otsu threshold segmentation method based on genetic algorithm was offered. According to the threshold and the gray scale values of pixels, the pixels were divided into two categories, and then the genetic algorithm was used to find the maximum variance between clusters and obtain the optimal threshold of segmentation image. The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

A Critical Overview of Image Segmentation Techniques Based on Transition Region

2018 ◽  
pp. 1308-1318
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Transition Region ◽  
Google Scholar ◽  
Research Directions ◽  
Distinctive Type ◽  
Critical Overview

Image segmentation is the key step from image processing to image analysis, and is an important technique of image engineering. Image segmentation based on transition region is a special or distinctive type of techniques that are different from traditional boundary-based or region-based techniques. Since the first technique using transition region proposed, there are many subsequent related researches and applications, and a series of papers in the literature citing are published worldwide. Using Google Scholar, a number of papers citing the original papers are searched, a study on the statistics of these papers is conducted. These papers are sorted first according to the publishing year, and then grouped according to their purposes and contents (with techniques used). Some questionable issues in these papers are pointed out and critically discussed, and several further research directions are indicated and analyzed.

Review of Otsu Segmentation Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1959-1961 ◽  
Author(s):  
Yan Xue Dong
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Simple Calculation ◽  
Segmentation Algorithm ◽  
Image Thresholding ◽  
Otsu Method

Image segmentation is the key step in the process from image processing to image analysis. Otsu method is one of the most successful methods for image thresholding because of its simple calculation. Otsu method can select threshold automatically and divide the object from the background in the image. In this paper, various Otsu algorithm are studied.

scholarly journals A Study on Image Segmentation Techniques

2019 ◽  
Vol 8 (S2) ◽  
pp. 75-78
Author(s):  
S. Abdul Saleem ◽  
G. Vinitha
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Video Frame ◽  
Challenging Problem ◽  
Digital Form ◽  
Low Level ◽  
Grey Scale ◽  
High Level

Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.

scholarly journals CAPABILITIES TO VISUALIZE THE OPERATING REGION OF SURGICAL INTERVENTION RELATIVELY TO CRANIAL LANDMARKS FOR NEURONAVIGATION

2016 ◽  
Vol 1 ◽  
pp. 21-30
Author(s):  
Oleg Avrunin ◽  
Maksym Tymkovych ◽  
Tetiana Kononenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Surgical Intervention ◽  
Outer Edge ◽  
Anatomical Landmarks ◽  
Practical Advice ◽  
Operating Region ◽  
Automated Determination ◽  
Selection Of

During the work we analyze the process of neuronavigation in terms of using different approaches for aligning the operational volume at surgical stage in respective to the preplanning data. The work is dedicated to capabilities to visualize the operating region of surgical intervention relatively to cranial landmarks for neuronavigation. We analyzed the principles of selection of anatomical landmarks. We give practical advice on the choice of anatomical landmarks with respect to system of image analysis. Based on processes of image analysis and image processing we show necessity of utilization specialized anatomical landmarks. At this research we propose utilize the cranial landmarks on outer edge of cranium. It was confirmed the possibility of their automated determination. Was shown the necessity of intracerebral landmarks binding to the cranial landmarks. The proposed approach to selection of anatomical landmarks can be applied in neuronavigation for simplification process of their extraction and their calculation.

Study of Relative Entropy Coefficients for Image Segmentation Based on Particle Swarm Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1056-1060
Author(s):  
You Rui Huang ◽  
Li Guo Qu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Relative Entropy ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Threshold Method ◽  
Swarm Optimization ◽  
Computation Algorithm

Image segmentation is the basis of image analysis, and because of its simplicity, rapidity and stability, the threshold method is the important one, applying in the image processing and recognition widely. In this paper, a new method is proposed, which based on relative entropy coefficients between random variables. It maximizes the target and background, which is the relative entropy coefficient in probability distribution, and gets the optimal threshold of image segmentation, and then optimizes it using particle swarm algorithm which is an evolutionary computation algorithm. The result of relative entropy coefficients for image segmentation proves its feasibility and better effect.

scholarly journals Brain Tumor Segmentation using Multi Level Thresholding using Fuzzy Entropy

2020 ◽  
Vol 8 (5) ◽  
pp. 2641-2643
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Entropy ◽  
Image Processing Technique ◽  
Tumor Segmentation ◽  
Fuzzy Approach ◽  
Segmentation Process ◽  
Multi Level

In image processing field, image processing technique is used to distinguish the object from its image scene at pixel level. The image segmentation process is the significant task in the processing of image field as well as in image analysis. The most difficult task in the image analysis field is the automatic separation of object from its background. To alleviate this problem several image segmentation process is introduced are gray level thresholding, edge detection method, interactive pixel classification method, neural network approach and segmentation based on fuzzy approach This chapter presents the multilevel set thresholding method using partition of fuzzy approach on brain image histogram and theory of entropy. The fuzzy entropy method is applied on multi-level brain tumor MRI image segmentation method. The threshold of brain MR image is obtained by optimized the entropy measure. In this method, Differential Evolution technique is used to find the best solution.

scholarly journals Estimation of Blast Severity on Rye and Triticale Spikes by Digital Image Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
João Leodato Nunes Maciel ◽  
Alfredo do Nascimento Junior ◽  
Cristina Boaretto
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Digital Image Analysis ◽  
Linear Regression Analysis ◽  
Reference Method ◽  
Coefficient Of Determination ◽  
Breeding Programs ◽  
True Value ◽  
Mouse Cursor ◽  
Selection Of

In Brazil, more efficient methods are a necessity for evaluating blast severity on spikes in the breeding programs of rye, triticale, wheat, and barley. The objective of this work was to determine the feasibility of assessing blast severity based on the analysis of digital images of symptomatic rye and triticale spikes. Triticale and rye genotypes were grown to anthesis in pots and were then inoculated with a mixture ofMagnaporthe oryzaeisolates. Blast severity on the spikes was evaluated visually and after that the spikes were detached and photographed. Blast severity was determined using the program ImageJ to analyze the obtained images. Two methods of image analysis were used: selection of symptomatic areas using a mouse cursor (SCU) and selection of symptomatic areas using image segmentation (SIS). The SCU method was considered the standard reference method for determining the true value of blast severity on spikes. An analysis of variance did not determine any difference among the evaluation methods. The coefficient of determination (R2) obtained from a linear regression analysis between the variables SIS and SCU was 0.615. The obtained data indicate that the evaluation of blast severity on spikes based on image segmentation is feasible and reliable.

Image Segmentation Research Based on Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 1622-1625
Author(s):  
Xiao Wei Guan ◽  
Xia Zhu
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Feature Representation ◽  
The Other ◽  
Ga Algorithm ◽  
Representation Method ◽  
Optimal Feature ◽  
Image Segmentation Algorithm

As one of the difficulties and hot of computer vision and image processing, Image segmentation is highly valued by the research workers. Yet there is no image segmentation algorithm which is generic, and it is difficult to obtain an optimal feature representation method. In this paper, genetic algorithm (GA) has proposed to segment the image. GA algorithm can improve the efficiency and quality of the picture some extent through the experimental results. The algorithm has some versatility, as long as the corresponding parameters are adjusted, it can also handle the other images. The results show that GA algorithm is very stable, and the fusion result is more satisfactory. Thus, GA can be applied in image segmentation and this algorithm will have good prospects in image processing.

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