@inproceedings{1c3b9ad562e94ba2a6b1d3f1b06883ad,
title = "Date-driven Based Image Enhancement for Segmenting of MS Lesions in T2-w and Flair MRI",
abstract = "This paper proposed a data-driven based image enhancement scheme to segment Multiple Sclerosis (MS) lesions. It utilizes a class-adaptive Gaussian Markov random field modelling (HMRF) and mutual information to automatic enhance the MS lesions. Then an alpha matting technique is used to refine the segmentation results. The advantages of the approach lies in its date-driven processing. It can automatically enhance the density of MS lesions, which is guided by calculating the mutual information value of the segmentation results in the successive steps. In addition, the partial volume effects are considered and the regions of interests are segmented in a sub-pixel precision. The experiments on real MR images show the proposed segmentation method can effectively segment MS lesions.",
keywords = "Date-driven, Enhancement, MS lesions, Mutual information, Segmentation",
author = "Ziming Zeng and Zhonghua Han and Yitian Zhao and Reyer Zwiggelaar",
year = "2014",
doi = "10.1007/978-94-007-7618-0_357",
language = "English",
isbn = "9789400776173",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Nature",
pages = "2827--2832",
editor = "Shaozi Li and Qun Jin and Xiaohong Jiang and Park, {James J. (Jong Hyuk)}",
booktitle = "Frontier and Future Development of Information Technology in Medicine and Education, ITME 2013",
address = "Switzerland",
}