A machine learning approach for brain image enhancement and segmentation. Issue 4 (20th November 2017)

• Record Type: Journal Article
• Authors: Fenshia Singh, J.; Magudeeswaran, V.
• Is Part Of: International journal of imaging systems and technology. Volume 27:Issue 4(2017)
• Page Start: 311
• Page End: 316
• Publication Date: 2017-11-20
• Subjects: contrast -- entropy -- histogram -- level‐set; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22234
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for surgical planning, etc. However, due to presence of noise and uncertainty between different tissues in the brain image, the segmentation of brain is a challenging task. This problem is rectified in this article using two stages. In the first stage an enhancement technique called contrast limited fuzzy adaptive histogram equalization (CLFAHE) which is a combination of CLAHE and fuzzy enhancement is used to improve the contrast of MRI Brain images. Contrast of the image is controlled using contrast intensification operator (Clip limit). The second stage deals with the segmentation of enhanced image. The enhanced brain images are segmented using new level‐set method which has the property of both local and global segmentation. Signed pressure force (SPF) function is also used here which stops the contours at weak and blurred edged efficiently.