Abstract
Skull stripping isolates brain from the non-brain tissues. It supplies major significance in medical and image processing fields. Nevertheless, the manual process of skull stripping is challenging due to the complexity of images, time consuming and prone to human errors. This paper proposes a qualitative analysis of skull stripping accuracy for Magnetic Resonance Imaging (MRI) brain images. Skull stripping of eighty MRI images is performed using Seed-Based Region Growing (SBRG). The skull stripped images are then presented to three experienced radiologists for visual qualitative evaluation. The level of accuracy is divided into five categories of “over delineation”, “less delineation”, “slightly over delineation”, “slightly less delineation” and “correct delineation”. Primitive statistical methods are calculated to examine the skull stripping performances. In another note, Fleiss Kappa statistical analysis is used to measure the agreement among radiologists. The qualitative performances analysis proved that the SBRG is an effective technique for skull stripping.
Published Version
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