Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Autor:	Nagaraja Perumal, Kalaiselvi Thiruvenkadam
Rok vydání:	2016
Předmět:	Discrete wavelet transform medicine.diagnostic_test Computer science business.industry Scale-space segmentation Magnetic resonance imaging 02 engineering and technology Fuzzy logic 030218 nuclear medicine & medical imaging Electronic Optical and Magnetic Materials 03 medical and health sciences 0302 clinical medicine Transformation (function) Wavelet 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Computer vision Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering Cluster analysis business Software
Zdroj:	International Journal of Imaging Systems and Technology. 26:305-314
ISSN:	0899-9457
DOI:	10.1002/ima.22202
Popis:	Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging MRI images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform DWT for pre- and post-processing, fuzzy c-means FCM for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter GM, white matter WM, cerebrospinal fluid CSF, and background BG. Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305-314, 2016
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::45055ef1b9bb5db6e4d80dbc222c7c5e https://doi.org/10.1002/ima.22202 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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