A K-Means-Galactic Swarm Optimization-Based Clustering Algorithm with Otsu's Entropy for Brain Tumor Detection
Image segmentation is a technique in order to segment an image into various parts and derive meaningful information out of each one. In this article, problem of image segmentation is applied on brain MRI images. This is done in order to detect and capture the location, size and shape of five differe...
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| Published in | Applied artificial intelligence Vol. 33; no. 2; pp. 152 - 170 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Philadelphia
Taylor & Francis
28.01.2019
Taylor & Francis Ltd Taylor & Francis Group |
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| Abstract | Image segmentation is a technique in order to segment an image into various parts and derive meaningful information out of each one. In this article, problem of image segmentation is applied on brain MRI images. This is done in order to detect and capture the location, size and shape of five different types of tumors. Here, image segmentation is viewed as an clustering problem and a new hybrid K-means Galatic Swarm Optimization (GSO) algorithm is proposed for effective solution. The Otsus entropy measure is used as the fitness function for deriving the segments. Extensive simulation studies with five performance measures on five different brain MRI images reveal the superior performance of the proposed approach over GSO, Real Coded Genetic Algorithm (RCGA), and K-Means clustering algorithms. |
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| AbstractList | Image segmentation is a technique in order to segment an image into various parts and derive meaningful information out of each one. In this article, problem of image segmentation is applied on brain MRI images. This is done in order to detect and capture the location, size and shape of five different types of tumors. Here, image segmentation is viewed as an clustering problem and a new hybrid K-means Galatic Swarm Optimization (GSO) algorithm is proposed for effective solution. The Otsus entropy measure is used as the fitness function for deriving the segments. Extensive simulation studies with five performance measures on five different brain MRI images reveal the superior performance of the proposed approach over GSO, Real Coded Genetic Algorithm (RCGA), and K-Means clustering algorithms. |
| Author | Chauhan, Rajat Dhaked, Uttam Modi, Rahul Nanda, Satyasai Jagannath Gulati, Ishank |
| Author_xml | – sequence: 1 givenname: Satyasai Jagannath surname: Nanda fullname: Nanda, Satyasai Jagannath email: nanda.satyasai@gmail.com organization: Department of Electronics and Communication Engineering, Malaviya National Institute of Technology – sequence: 2 givenname: Ishank surname: Gulati fullname: Gulati, Ishank organization: Department of Electronics and Communication Engineering, Malaviya National Institute of Technology – sequence: 3 givenname: Rajat surname: Chauhan fullname: Chauhan, Rajat organization: Department of Electronics and Communication Engineering, Malaviya National Institute of Technology – sequence: 4 givenname: Rahul surname: Modi fullname: Modi, Rahul organization: Department of Electronics and Communication Engineering, Malaviya National Institute of Technology – sequence: 5 givenname: Uttam surname: Dhaked fullname: Dhaked, Uttam organization: Department of Electronics and Communication Engineering, Malaviya National Institute of Technology |
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| SubjectTerms | Algorithms Brain Cluster analysis Clustering Computer simulation Entropy of solution Fitness Genetic algorithms Image detection Image segmentation Magnetic resonance imaging Medical imaging Optimization Tumors Vector quantization |
| Title | A K-Means-Galactic Swarm Optimization-Based Clustering Algorithm with Otsu's Entropy for Brain Tumor Detection |
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