Abstract
Skull stripping is the process of isolating brain from non-brain tissues. It supplies major significance in both 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. The skull stripping of eighty MRI images is performed using Seed-Based Region Growing (SBRG). The skull stripped images are then presented to three experienced radiographers to visually evaluate the level of skull stripping accuracy. The level of accuracy is divided into five categories which are “over delineation”, “less delineation”, “slightly over delineation”, “slightly less delineation” and “correct delineation”. Primitive statistical methods of mode, mean and standard deviation are calculated to examine the qualitative performances of skull stripping capability. In another note, Fleiss Kappa statistical analysis is used to measure the agreement among the radiographers. The qualitative performances analysis proved that the SBRG is an effective technique for skull stripping. The reliability of agreement significances among the radiographers is found to be substantial.
Published Version
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