Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector

Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, a mixture...

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Bibliographic Details
Published in:Journal of medical systems 2018-12, Vol.42 (12), p.251-6, Article 251
Main Authors: Arokia Jesu Prabhu, L., Jayachandran, A.
Format: Article
Language:English
Subjects:
Advancements in Internet of Medical Things for Healthcare System
Brain
Brain cancer
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - pathology
Brain tumors
Cancer
Classification
Cytology
Feature extraction
Fuzzy Logic
Health Informatics
Health Sciences
Humans
Image & Signal Processing
Image analysis
Image classification
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical morphology
Medical imaging
Medicine
Medicine & Public Health
Meninges
Meningioma
Meningioma - diagnostic imaging
Meningioma - pathology
Neuroimaging
Nuclei (cytology)
Spine
Statistics for Life Sciences
Support Vector Machine
Tumors
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Summary:Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells’ boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. Accurate cancer cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation..An adaptive binarization technique is an important step for medical image analysis.Finally, a novel hybrid Fuzzy SVM is designed in the classification stage meningioma brain tumor. The tumor classification results of proposed feature extraction with SVM is 74.24%, MM with FSVM is 82.67% and MM with RBF is 62.71% and our proposed method MM with Hybrid SVM is 91.64%.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-018-1094-3