Brain segmentation in magnetic resonance human head scans using multi-seeded region growing. (June 2014)

• Record Type: Journal Article
• Authors: Somasundaram, K.; Kalavathi, P.
• Is Part Of: Imaging science journal. Volume 62:Number 5(2014)
• Page Start: 273
• Page End: 284
• Publication Date: 2014-06
• Subjects: Imaging systems -- Periodicals; 621.36705
• DOI: 10.1179/1743131X13Y.0000000068
• Languages: English
• ISSNs: 1368-2199

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <p>This paper presents a skull stripping method to segment the brain from MRI human head scans using multi-seeded region growing technique. The proposed method has two stages. In Stage-1, the brain in the middle slice is segmented, the brains in the remaining slices are segmented in Stage-2. In each stage, the proposed method is required to identify the rough brain mask. The fine brain region in the rough brain mask is segmented using multi-seeded region growing approach. The proposed method uses multiple seed points which are selected automatically based on the intensity profile of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) of the brain image. The proposed brain segmentation method using multi-seeded region growing (BSMRG) was validated using 100 volumes of T1, T2 and PD-weighted MR brain images obtained from Internet Brain Segmentation Repository (IBSR), LONI and Whole Brain Atlas (WBA). The best Dice (<italic>D</italic>) value of 0·971 and Jaccard (<italic>J</italic>) value of 0·944 were recorded by the proposed BSMRG method on IBSR dataset. For LONI dataset, the best values of <italic>D</italic> = 0·979 and <italic>J</italic> = 0·960 were obtained for the sagittal oriented images by the proposed method. The performance consistency of the proposed method was tested on the brain images of all types and orientation and have and produced better and stable results than the existing methods Brain Extraction Tool (BET), Brain Surface Extraction (BSE), Watershed Algorithm (WAT), Hybrid Watershed Algorithm (HWA) and Skull Stripping using Graph Cuts (GCUT).</p> </abstract>