Multilevel Image Segmentation Using Modified Genetic Algorithm (MfGA)-based Fuzzy C-Means

2017 ◽  
pp. 1-24
Author(s):  
Sourav De ◽  
Sunanda Das ◽  
Siddhartha Bhattacharyya ◽  
Paramartha Dutta
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fuzzy C Means ◽  
Modified Genetic Algorithm

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

2021 ◽  
pp. 164-196
Author(s):  
Sunanda Das ◽  
Sourav De ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Execution Time ◽  
Color Image ◽  
Computational Cost ◽  
Cost Effective ◽  
Color Image Segmentation ◽  
Modified Genetic Algorithm ◽  
True Color Image ◽  
True Color

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

2018 ◽  
pp. 55-94
Author(s):  
Sunanda Das ◽  
Sourav De ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Execution Time ◽  
Color Image ◽  
Computational Cost ◽  
Cost Effective ◽  
Color Image Segmentation ◽  
Modified Genetic Algorithm ◽  
True Color Image ◽  
True Color

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

scholarly journals Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Omid Jamshidi ◽  
Abdol Hamid Pilevar
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fitness Function ◽  
Automatic Segmentation ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Mr Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Right ◽  
Number Of Segments

Magnetic resonance imaging (MRI) segmentation is a complex issue. This paper proposes a new method for estimating the right number of segments and automatic segmentation of human normal and abnormal MR brain images. The purpose of automatic diagnosis of the segments is to find the number of divided image areas of an image according to its entropy and with correctly diagnose of the segment of an image also increased the precision of segmentation. Regarding the fact that guessing the number of image segments and the center of segments automatically requires algorithm test many states in order to solve this problem and to have a high accuracy, we used a combination of the genetic algorithm and the fuzzy c-means (FCM) method. In this method, it has been tried to change the FCM method as a fitness function for combination of it in genetic algorithm to do the image segmentation more accurately. Our experiment shows that the proposed method has a significant improvement in the accuracy of image segmentation in comparison to similar methods.

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