Qualitative Analysis of Skull Stripping Accuracy for MRI Brain Images

  • Shafaf Ibrahim
  • Noor Elaiza Abdul Khalid
  • Mazani Manaf
  • Mohd Ezane Aziz
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 214)


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.


Qualitative analysis Skull stripping Seed-based region growing Medical imaging Magnetic resonance imaging 



Thousand thanks to Hospital Sungai Buloh for their full cooperation during the collection of MRI brain images. Special thanks to Dr Mohd Ezane Aziz, Dr Win Mar Jalaluddin and Dr Nik Munirah Nik Mahdi, the radiologists involved in the qualitative analysis. Finally, thanks to Research Management Institute (RMI), UiTM and financial support from ERGS-grant (600-RMI/ST/ERGS 5/3/(6/2011)) under the Department of Higher Education, Malaysia.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shafaf Ibrahim
    • 1
  • Noor Elaiza Abdul Khalid
    • 1
  • Mazani Manaf
    • 1
  • Mohd Ezane Aziz
    • 2
  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARASelangorMalaysia
  2. 2.Department of Radiology, Health CampusUniversiti Sains MalaysiaKelantanMalaysia

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