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A Generic Framework for Medical Image Segmentation

Agam Adityas Nugroho

Published by LAP Lambert Academic Publishing
ISBN 10: 3848442558 / ISBN 13: 9783848442553
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Title: A Generic Framework for Medical Image ...

Publisher: LAP Lambert Academic Publishing

Binding: Paperback

Book Condition: New

Book Type: Paperback

Description:

Paperback. 56 pages. Dimensions: 8.7in. x 5.9in. x 0.1in.Medical image segmentation is challenging and requires more sophisticated algorithms. Medical images are obtained by several different modalities and each of them has certain characteristics. For this reason, there is a need for developing a method which works for any kind of medical images. Deformable model is one of the most popular method in medical segmentation that requires some features (such as an edge) to be present along the boundary of the object, and pull the deformable curve toward that feature. This methods may be sensitive to the starting position and may leak through the boundary of the object if the edge feature is not salient enough in certain regions in the image. On the other hand, level set method evolves a contour implicitly by manipulating a level set function . In this book, we implemented level set region-based method to segment several images from different modalities. Its performance is tested by comparing the segmented results with those obtained by manual segmentation. The method reached a good performance with an average accuracy 94. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783848442553

About this title:

Synopsis: Medical image segmentation is challenging and requires more sophisticated algorithms. Medical images are obtained by several different modalities and each of them has certain characteristics. For this reason, there is a need for developing a method which works for any kind of medical images. Deformable model is one of the most popular method in medical segmentation that requires some features (such as an edge) to be present along the boundary of the object, and pull the deformable curve toward that feature. This methods may be sensitive to the starting position and may leak through the boundary of the object if the edge feature is not salient enough in certain regions in the image. On the other hand, level set method evolves a contour implicitly by manipulating a level set function .In this book, we implemented level set region-based method to segment several images from different modalities. Its performance is tested by comparing the segmented results with those obtained by manual segmentation.The method reached a good performance with an average accuracy 94%.

About the Author: Education : Gunadarma University : Bachelor of Electrical Engineering, Cum Laude,College Scholar 2006-2010University of Burgundy : Masters of Computer Science, National Ministry of Education's Scholar 2010-2011. Professional Exp. : Professor, Computer Department 2011--. Responsible for teaching beginning and advanced programming and networking.

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