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Paper
Title

A Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis

Year: 1399
COI: JR_JACET-6-1_003
Language: EnglishView: 314
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Authors

Aref Safari - Department of Computer Engineering, Islamic Azad University of Rasht, Rasht, Iran
Danial Barazandeh - Department of Computer Engineering, Islamic Azad University, Rasht Branch
Seyed Ali Khalegh Pour - Department of Computer Engineering, Islamic Azad University, Rasht Branch

Abstract:

Accurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the intensity of the disease. The applied method first employed feature selection algorithms to extract features from images, and then followed by applying a median filter to reduce the dimensions of features. The brain MRI offers a valuable method to perform pre-and-post surgical evaluations, which are keys to define procedures and to verify their effects. The reduced dimension was submitted to a diagnosis algorithm. We retrospectively investigated a total of 19 treatment plans, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. The simulation results demonstrate that the proposed algorithm is promising.

Keywords:

Paper COI Code

This Paper COI Code is JR_JACET-6-1_003. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/1030108/

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Safari, Aref and Barazandeh, Danial and Khalegh Pour, Seyed Ali,1399,A Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis,https://civilica.com/doc/1030108

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  • Parra, C.A., K. Iftekharuddin, and R. Kozma, Automated brain data ...
  • Ain, Q., M.A. Jaffar, and T.-S. Choi, Fuzzy anisotropic diffusion ...
  • Sachdeva, J., et al., A package-SFERCB- Segmentation, feature extraction, reduction ...
  • Isola, R., R. Carvalho, and A.K. Tripathy, Knowledge Discovery in ...
  • Shah, S. and S. Parikh. Issues in medical diagnosis using ...
  • Padma, A. and R. Sukanesh, SVM based classification of soft ...
  • Rajendran, P., M. Madheswaran, and K. Naganandhini. An improved pre-processing ...
  • Alirezaie, J., M. Jernigan, and C. Nahmias, Automatic segmentation of ...
  • Middleton, I. and R.I. Damper, Segmentation of magnetic resonance images ...
  • Li, Y., et al. Segmentation of brain magnetic resonance images ...
  • Hosseini, R., M. Mazinani, and A. Safari, A novel type-2 ...
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    Type of center: Azad University
    Paper count: 6,220
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