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Pambudi, Elindra Ambar
- Image Segmentation Analysis Based on K-means PSO by Using Three Distance Measures
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Authors
Affiliations
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, ID
2 Informatic Engineering, Universitas Dian Nuswantoro, ID
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, ID
2 Informatic Engineering, Universitas Dian Nuswantoro, ID
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1821-1826Abstract
The image segmentation is a technique of image processing which divides image into segments. The many proposed image segmentation techniques, k-Means clustering has been one of the basic image segmentation techniques. The advantages of k-Means are easy calculation, the number of small iteration, and one of the most commonly used clustering algorithm. but, The main problem in this algorithm is sensitive to selection initial cluster center. In this research, we present two approaches method which are used to execute image. It is PSO and k-Means. k-Means integrated with Particle Swarm Optimization (PSO) to improve the accuracy. The purpose of this research to find the effect of PSO towards k-Means in order to get the best selection initial cluster center. This research has been implemented using matlab and taking image dataset from weizzmann institute. The Result of our experiment, we have different result RMSE of k-Means PSO. Euclidean has less RMSE value than Manhattan. The difference RMSE between Euclidean PSO and Manhattan PSO only four point. but if we compare by processing time we have significant difference.Keywords
Vision Computing, Image Processing, Segmentation, Swarm Intelligence, Computer Science.References
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- Enhanced K-Means by Using Grey Wolf Optimizer for Brain MRI Segmentation
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Authors
Affiliations
1 Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, ID
1 Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, ID
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2353-2358Abstract
Segmentation is an essential part of the detection and classification series. The best result of brain MRI detection was followed by the best segmentation process. Supporting brain MRI detection accurately, one of the ways could be used by increasing segmentation. This paper utilizes one of the segmentation methods which is called clustering. We propose a clustering approach using K-Means. K-Means has advantages easy to understand, fast process, and guarantees convergence. But it has drawbacks which are initialization cluster center randomly, sometimes it is given good results but sometimes it is not. Therefore, this research proposes to optimize the weak side of K-Means using a grey wolf optimizer. Initialization cluster center was chosen based on fitness value. The fitness value of this paper is Sum Square Error (SSE), we purpose to minimize the SSE of the population and searching new positions depend on Gray Wolf Optimization (GWO)’s rule. The final position of GWO would be initialized by K-Means. The series of our research steps are acquisition image, grayscaling, resizing, segmentation, and analysis performance based on MSE and PSNR. The best result of the purposed method is k=17 which PSNR (16.09) and MSE (15.99). GWO K-Means were given the best outcome segmentation brain MRI based on measuring error value and PSNR.Keywords
Gray Wolf Optimization, K-Means, MRI Segmentation, Sum Square Error.References
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