Efficient brain tumor segmentation using OTSU and K-means clustering in homomorphic transform

Abstract

In computer vision, image segmentation technology plays a vital role in computer-aided diagnostic systems to precisely and accurately identify the area to be treated. Practically, it is usually not interested in all image parts, but only the required characteristics. Therefore, image segmentation algorithms can bound the cancerous cells in boxes to determine the severity of the cancer and get more clear results. This paper examined an efficient approach based on local contrast enhancement using the OTSU segmentation and K-means clustering segmentation in different transform domains for localizing the brain tumor area at a competitive time. The transform domains are providing better representation for images to be segmented and enabling much feature information for extraction. Moreover, adaptive histogram equalization with the local contrast enhancement has been improved the whole performance of the proposed approach. The proposed enhanced segmentation approaches have been tested with different datasets and evaluated using various quality metrics such as; accuracy, sensitivity, precision, specificity, MCC, F-measure, Dice, Jaccard, and the processing time. The proposed segmentation approaches in homomorphic transform and adaptive histogram equalization have conducted an efficient and reliable performance with high precision in the brain tumor localization. Moreover, the proposed algorithms have implemented for both 2D and 3D images and can provide higher efficiency and better image quality with both 2D and 3D image types achieving better robustness and proving that the proposed algorithms don’t depend on the input datasets.