Performance Assessment of Active Contour Models | for image segmentation

Hum Yan Chai (u. a.)

ISBN 10: 3847320807 / ISBN 13: 9783847320807

Published by LAP Lambert Academic Publishing, 2012

About this Item

Performance Assessment of Active Contour Models | for image segmentation | Hum Yan Chai (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783847320807 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106588297

Bibliographic Details

• Title: Performance Assessment of Active Contour Models | for image segmentation
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3847320807
• ISBN 13: 9783847320807
• Binding: Taschenbuch
• Condition: Neu
• Seller catalogs:
  • Bücher

About this title

Synopsis

Image segmentation is one of the significant techniques in image processing to distinguish desired parts from its background for further analysis. It provides visual means for inspection of anatomical structure of human body, identification of disease, tracking of its development and input for surgical planning and simulation. Active contour models are regarded as promising and vigorously research model-based approach to computer assisted medical image analysis. However, it is not trivial to assess whether one segmentation algorithm performs more superior than the other. Therefore, a systematic assessment tool is designed and implemented to examine all the important aspects of active contour models. Meanwhile, a novel supervised evaluator including analytical method and empirical methods are proposed to acts as objective evaluator. The obtained results highlighted both the strengths and limitations of the studied active contour models.

About the Author

is a present researcher in the field of biomedical engineering. He is working as a research member in Centre for Biomedical Engineering in Universiti Teknologi Malaysia(UTM). His research interests are Digital X-Ray imaging, medical image processing, artificial intelligence, fuzzy logic, and medical computing and performance optimization.