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.

scholarly journals An improved method of microscopic image segmentation

2018 ◽  
Vol 176 ◽  
pp. 01041
Author(s):  
Zhang Feng Shou ◽  
Dong Fang ◽  
Liu Jian Ting ◽  
Meng Xin
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Microscopic Image ◽  
Two Dimensional ◽  
Segmentation Method ◽  
Improved Method ◽  
Image Optimization ◽  
Segmentation Image

In order to improve the effectiveness and accuracy of image processing in modern medical inspection, a segmentation image optimization algorithm of improved two-dimensional maximum entropy threshold based on genetic algorithm combined with mathematical morphology is proposed, in view of the microscopic cell images characteristic and the shortcomings of the traditional segmentation algorithm. Through theoretical analysis and contrast test, the segmentation method proposed is superior to the traditional threshold segmentation method in microscopic cell images, and the average segmentation time of the improved algorithm is 73% and 44% higher than the traditional two-dimensional maximum entropy threshold and the improved two-dimensional maximum entropy threshold.

A Video Image Segmentation Technology Based on Adaptive Thresholding Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1189-1192 ◽  
Author(s):  
Hai Jun Zhao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Variance Decomposition ◽  
Optimum Number ◽  
Segmentation Method ◽  
Class Difference ◽  
Classification Image ◽  
Maximum Variance ◽  
Important Position ◽  
Thresholding Algorithm

Image segmentation is a key step in image processing and image analysis and occupies an important position in image engineering.In this paper, basing on maximum variance between-class, an adaptive and multi-objective image segmentation method is proposed. The concrete implement is to determine adaptively the optimum number of threshold of image using the idea of variance decomposition,while calculating the weighted ratio of within class difference and class difference existing in each classification image. By comparing the ratio, the optimum number of target for image can be get. The experimental results show that the sub-images after segmentation are relatively clear and the differences between classes are obvious.

Test Methods Research of Cracks in Ceramics Based on Genetic Algorithm

2014 ◽  
Vol 977 ◽  
pp. 25-29
Author(s):  
Bing Xiang Liu ◽  
Feng Qin Wang ◽  
Xu Dong Wu ◽  
Ying Xi Li
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Optimization Problem ◽  
Fitness Function ◽  
Recognition Rate ◽  
Test Methods ◽  
Segmentation Method ◽  
Maximum Variance ◽  
Best Value ◽  
Crossover And Mutation

In order to improve the reliability of cracks in ceramics test, this paper puts forward a target adaptive segmentation method used by genetic algorithm and maximum-variance algorithm in all classes. This proposed method makes some appropriate improvements about crossover and mutation in genetic algorithm. Besides, the fitness function draws merits of maximum-variance algorithm in all classes and turns the best value in image segmentation into corresponding optimization problem. The simulation results of experiment shows the method proposed shortens the searching time and strengthens anti-noise property during image segmentation and improves recognition rate of cracks in ceramics.

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.

An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

2012 ◽  
Vol 490-495 ◽  
pp. 1251-1255 ◽  
Author(s):  
Hong Cai ◽  
Xue Yuan Zhang ◽  
Hai Tao Dai ◽  
Dong Ming Zhou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Adaptive Algorithm ◽  
Experimental Results ◽  
Edge Extraction ◽  
Segmentation Method ◽  
Adaptive Parameters ◽  
Selection Of ◽  
Better Than

PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.

Least-Squares Halftoning Algorithm Based on Image Segmentation

2012 ◽  
Vol 263-266 ◽  
pp. 2082-2087
Author(s):  
Zi Fen He ◽  
Zhao Lin Zhan ◽  
Yin Hui Zhang
Keyword(s):  
Image Segmentation ◽  
Least Squares ◽  
Performance Measure ◽  
Distance Metrics ◽  
Digital Halftoning ◽  
Segmentation Method ◽  
Halftone Image ◽  
Printer Model ◽  
Halftone Images ◽  
Selection Of

This work presents a method based on the image content for digital halftoning using K-means clustering theory. Our algorithm applies to both a printer model and a model for the human visual system (HVS). The method strives to minimize the perceived error between the continuous original image and the halftone image. First, the gray image is partitioned into two, three and four regions using K-means image segmentation method, whose performance depends on the selection of distance metrics. Next, the statistics of average gray value of each clustering is calculated. Each clustering uses the least-squares model-based(Lsmb) algorithm to obtain halftone image. Finally, analysis and simulation results show that the proposed algorithm produces better gray-scale halftone image quality when we increase the number of clustering with a certain range. A performance measure for halftone images is used to evaluate our algorithm. The value of MSEv, WSNR and PSNR for two partitions is almost the same as that of the Lsmb algorithm, but for three and four partitions that the proposed algorithm achieves consistently better values of MSEv, WSNR and PSNR than the Lsmb algorithm.

The Location of Cancer Area Based on Region Growing Algorithm in Medical Image

2013 ◽  
Vol 760-762 ◽  
pp. 1552-1555 ◽  
Author(s):  
Jing Jing Wang ◽  
Xiao Wei Song ◽  
Mei Fang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Processing ◽  
Region Growing ◽  
Challenging Problem ◽  
Correct Judgment ◽  
Threshold Segmentation ◽  
Seed Points

Image segmentation in medical image processing has been extensively used which has also been applied in different fields of medicine to assist doctors to make the correct judgment and grasp the patient's condition. However, nowadays there are no image threshold segmentation techniques that can be applied to all of the medical images; so it has became a challenging problem. In this paper, it applies a method of identifying edge of the tissues and organs to recognize its contour, and then selects a number of seed points on the contour range to locate the cancer area by region growing. And finally, the result has demonstrated that this method can mostly locate the cancer area accurately.

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