Title :
A simple skull stripping algorithm for brain MRI
Author :
Roy, Shaswati ; Maji, Pradipta
Author_Institution :
BIB Lab., Indian Stat. Inst., Kolkata, India
Abstract :
The skull stripping method is an important area of study in brain image processing applications. It acts as preliminary step in numerous medical applications as it increases speed and accuracy of diagnosis in manifold. It removes non-cerebral tissues like skull, scalp, and dura from brain images. In this regard, a simple skull stripping algorithm, termed as S3, is proposed in this paper, which is based on brain anatomy and image intensity characteristics. The proposed S3 method is unsupervised and knowledge based. It uses adaptive intensity thresholding followed by morphological operations, for increased robustness, on brain magnetic resonance (MR) images. The threshold value is adaptively calculated based on the knowledge of intensity distribution in brain MR images. Experimental results, both qualitative and quantitative, are reported on a set of synthetic and real brain MR T1-weighted images. The performance of the proposed S3 algorithm is compared with that of three popular methods, namely, brain extraction tool (BET), brain surface extractor (BSE), and robust brain extraction (ROBEX) using standard validity indices.
Keywords :
biomedical MRI; brain; image segmentation; medical image processing; BET; BSE; ROBEX; adaptive intensity thresholding; brain MRI; brain anatomy; brain extraction tool; brain image processing applications; brain magnetic resonance images; brain surface extractor; dura; image intensity characteristics; intensity distribution knowledge; medical applications; morphological operations; noncerebral tissues; qualitative analysis; quantitative analysis; real brain MR T1-weighted images; robust brain extraction; scalp; simple-skull stripping algorithm; skull; standard validity indices; synthetic brain MR T1-weighted images; threshold value; unsupervised-knowledge based S3 method; Brain modeling; Image segmentation; Magnetic resonance imaging; Morphology; Robustness; Surface morphology; Magnetic resonance imaging; mathematical morphology; skull stripping; thresholding;
Conference_Titel :
Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on
Conference_Location :
Kolkata
DOI :
10.1109/ICAPR.2015.7050671