Please use this identifier to cite or link to this item:
|Title:||Medical image segmentation using watershed segmentation with texture-based region merging||Authors:||Ng, H.P.
|Issue Date:||2008||Citation:||Ng, H.P.,Huang, S.,Ong, S.H.,Foong, K.W.C.,Goh, P.S.,Nowinski, W.L. (2008). Medical image segmentation using watershed segmentation with texture-based region merging. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" : 4039-4042. ScholarBank@NUS Repository.||Abstract:||The use of the watershed algorithm for image segmentation is widespread because it is able to produce a complete division of the image. However, it is susceptible to over-segmentation and in medical image segmentation, this meant that that we do not have good representations of the anatomy. We address this issue by thresholding the gradient magnitude image and performing post-segmentation merging on the initial segmentation map. The automated thresholding technique is based on the histogram of the gradient magnitude map while the post-segmentation merging is based on the similarity in textural features (namely angular second moment, contrast, entropy and inverse difference moment) belonging to two neighboring partitions. When applied to the segmentation of various facial anatomical structures from magnetic resonance (MR) images, the proposed method achieved an overlap index of 92.6% compared to manual contour tracings. It is able to merge more than 80% of the initial partitions, which indicates that a large amount of over-segmentation has been reduced. Results produced using watershed algorithm with and without the proposed and proposed post-segmentation merging are presented for comparisons. © 2008 IEEE.||Source Title:||Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"||URI:||http://scholarbank.nus.edu.sg/handle/10635/70911||ISBN:||9781424418152|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 8, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.