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Fusion Deformable Template with Disc...

Liu, Cheng-Yi.

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Fusion Deformable Template with Discriminative Models for Robust Three-Dimensional Brain Image Segmentation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fusion Deformable Template with Discriminative Models for Robust Three-Dimensional Brain Image Segmentation./
作者:	Liu, Cheng-Yi.
面頁冊數:	100 p.
附註:	Source: Dissertation Abstracts International, Volume: 73-09(E), Section: B.
Contained By:	Dissertation Abstracts International73-09B(E).
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3515080
ISBN:	9781267406316

Fusion Deformable Template with Discriminative Models for Robust Three-Dimensional Brain Image Segmentation.
Liu, Cheng-Yi.

Fusion Deformable Template with Discriminative Models for Robust Three-Dimensional Brain Image Segmentation. - 100 p.

Source: Dissertation Abstracts International, Volume: 73-09(E), Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2011.

Automatically segmenting brain anatomical structures from 3D MRI images is an important task in medical imaging. One major challenge in this problem is to design/learn effective models (for both intensities and shapes) accounting for the large image variation due to the acquisition processes by different machines, at different parameters, and for different subjects. Deformable template models (mostly generative) study the explicit parameters for matching the images with the templates, and thus are robust against the global intensity changes; discriminative models are able to combine many local image statistics to capture the complex image patterns. In this paper, we propose a robust brain image segmentation algorithm by fusing together deformable templates and informative features. We tested our method on a few popular datasets of a large number of images and show advantages over several existing state-of-the-art systems.

ISBN: 9781267406316Subjects--Topical Terms:

535387
Biomedical engineering.

Fusion Deformable Template with Discriminative Models for Robust Three-Dimensional Brain Image Segmentation.
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