Paper Info
Title
Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images.
Abstract
To isolate the brain from non-brain tissues using a fully automatic method may be affected by the presence of radio frequency non-homogeneity of MR images (MRI), regional anatomy, MR sequences, and the subjects of the study. In order to automate the brain tumor (Glioblastoma) detection, we proposed a novel approach of skull stripping for axial slices derived from MRI. Then, the brain tumor was detected using multi-level threshold segmentation based on histogram analysis. Skull-stripping method, was applied by adaptive morphological operations approach. This is considered an empirical threshold by calculation of the area of brain tissue, iteratively. It was employed on the registration of non-contrast T1-weighted (T1-WI) and its corresponding fluid attenuated inversion recovery sequence. Then, we used multi-thresholding segmentation (MTS) method which is proposed by Otsu. We calculated the performance metrics based on the similarity coefficients for patients (n = 120) with tumor. The adaptive algorithm of skull stripping and MTS of segmented tumors were achieved efficient in preliminary results with 92 and 80 % of Dice similarity coefficient and 0.3 and 25.8 % of false negative rate, respectively. The adaptive skull stripping algorithm provides robust skull-stripping results, and the tumor area for medical diagnosis was determined by MTS.
Year
DOI
Venue
2016
10.1007/s40708-016-0033-7
Brain Informatics
Keywords
Field
DocType
Image segmentation,MRI brain,Similarity measure,Skull stripping,Tumor
Histogram,Similarity measure,Pattern recognition,Segmentation,Computer science,Fluid-attenuated inversion recovery,Brain tumor,Image segmentation,Artificial intelligence,Adaptive algorithm,Medical diagnosis
Journal
Volume
Issue
ISSN
3
1
2198-4026
Citations 
PageRank 
References 
3
0.38
17
Authors
2
Name
Order
Citations
PageRank
Ahmad Chaddad13811.39
Camel Tanougast212225.44