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Performance Analysis for Efficient Brain Tumor Segmentation by using Clustering Algorithm

Affiliations

  • Department of Electronics and Communication Engineering, KL University, Guntur − 522502, India
  • MVR College of Engineering and Technology, Vijayawada Rural, Paritala − 521180, Andhra Pradesh, India
  • Department of ECE, Jawaharlal Nehru Technological University, Kakinada − 533003, Andhra Pradesh, India

Abstract


Objective: Normally MRI scan or CT helps to view the biology of brain. The segmentation methods are used to identify the tumor size and location. Methods/Analysis: Some of the segmentation methods are the Histogram-based segmentation and the Region-based segmentation (e.g.: Edge Detection method) which have the drawbacks in detection of size of the tumor and region. We are using the clustering based segmentation algorithms in this project. The run time and efficiency are the parameters used for comparison. Findings: These clustering algorithms like K-means, Fuzzy C and Pillar means are compared to each other for better performance by calculating the run time and efficiency of algorithms. This attempt improves the efficiency and computing time. Application/Improvements: It may help pathologists to identify the exact size and region easily

Keywords

Fuzzy C, K-means, Pathologists, Pillar means, Segmentation

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References


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