scholarly journals Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning

2017 ◽  
Vol 05 (01) ◽  
pp. 90-96 ◽  
Author(s):  
Xianqi Cao ◽  
Jiaqing Miao ◽  
Yu Xiao
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Dictionary Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Improved Genetic Algorithm

scholarly journals Incorporating priors for medical image segmentation using a genetic algorithm

2016 ◽  
Vol 195 ◽  
pp. 181-194 ◽  
Author(s):  
Payel Ghosh ◽  
Melanie Mitchell ◽  
James A. Tanyi ◽  
Arthur Y. Hung
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

2019 ◽  
Vol 8 (4) ◽  
pp. 39-59
Author(s):  
Shashwati Mishra ◽  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Novel Approach ◽  
Fitness Value ◽  
Sensitivity Specificity

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

2021 ◽  
pp. 896-916
Author(s):  
Shashwati Mishra ◽  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Novel Approach ◽  
Sensitivity Specificity ◽  
Processing Techniques

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

scholarly journals Medical Image Segmentation using Genetic Algorithm

2013 ◽  
Vol 81 (18) ◽  
pp. 10-15 ◽  
Author(s):  
Divya Kaushik ◽  
Utkarsha Singh ◽  
Paridhi Singhal ◽  
Vijai Singh
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation
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