A Computationally Efficient Skull Scraping Approach for Brain MR Image

Abstract

Background: In the process of volumetric evaluation of the damaged region in the human brain from a MR image it is very crucial to remove the non-brain tissue from the acquainted image. At times there is a chance during the process of assessing the damaged region through automated approaches might misinterpret the non-brain tissues like skull as damaged region due to their similar intensity features. So in order to address such issues all such artefacts. Objective: In order to mechanize an efficient approach that can effectively address the issue of removing the non-brain tissues with minimal computation effort and precise accuracy. It is very essential to keep the computational time to be as minimal as possible because the processes of skull removal is used in conjunction with segmentation algorithm, and if the skull scrapping approach has consumed a considerable amount of time, they it would impact the over segmentation and volume assessment time which is not advisable. Methods: In this paper a completely novel approach named Structural Augmentation has been proposed, that could efficiently remove the skull region from the MR image. The proposed approach has several phases that include applying of Hybridized Contra harmonic and Otsu AWBF filtering for noise removal and threshold approximation through Otsu based approach and constructing the bit map based on the approximated threshold. Morphological close operation followed by morphological open operation with reference to a structural element through the generated bitmap image. Results: The experiment are carry forwarded on a real time MR images of the patient at KGH hospital, Visakhapatnam and the images from open sources repositories like fmri. The experiment is conducted on the images of varied noise variance that are tabulated in the results and implementation section of the article. The accuracy of the proposed method has been evaluated through metrics like Accuracy, Sensitivity, Specificity through true positive, true negative, False Positive and False negative evaluations. And it is observed that the performance of the proposed algorithm seems to be reasonable good. Conclusion: The skull scrapping through structural Augmentation is computationally efficient when compared with other conventional approaches concerning both computational complexity and the accuracy that could be observed on experimentation. The Adaptive Weighted Bilateral Filter that acquire the weight value from the approximated contra harmonic mean will assist in efficient removal of poison noised by preserving the edge information and Otsu algorithm is used to determine the appropriate threshold value for constructing the bitmap image of the original MRI image which is efficient over the earlier mean based approach for estimating the threshold. Moreover, the efficiency of the proposed approach could be further improved by using customized structural elements and incorporating the fuzzy based assignments among the pixels that belong to brain tissue and skull effectively.