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Ultrasonic heart image segmentation using active contour model Open Access


Other title
active contour model
image segmentation
ultrasonic ventricle image
contour validation
Type of item
Degree grantor
University of Alberta
Author or creator
Sun, Jiuyu
Supervisor and department
Ray, Nilanjan (Computing Science)
Zhang, Hong (Computing Science)
Examining committee member and department
Zhang, Hong (Computing Science)
Ray, Nilanjan (Computing Science)
Yang, Herb (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
This thesis is concerned with the ultrasonic heart image segmentation problem using parametric active contour model. Most of the existing parametric models consider only either the edge or the regional information. In this thesis, we propose a new parametric active contour model considering both the edge and regional information. The model is based on the vector field convolution (VFC) model and the Chan-Vese model and thus it is called as VFCCV model. The VFCCV not only has a large capture range and the ability to detect concavity, but is also robust to noise, cluttered image background, and weak boundary. Experiments on synthetic images, heart ultrasound images and MRI images show VFCCV has better performance than either VFC or Chan-Vese, and a state-of-the-art model combining edge and region information. In order to use VFCCV to segment the 3D ventricle ultrasound image, the 3D image is divided into 2D slices, and we segment each slice using VFCCV. However, the segmentation results of some slices may be inaccurate, while manual validation for every slice would be tedious. Therefore, we propose a novel automatic contour validation method, and introduce the validation step in the segmentation system. The validation step can judge whether the contour on each slice is accurate in order to make the whole process as automatic as possible while keeping a high segmentation accuracy. We extend an existing method that is based on the principal component analysis (PCA) for contour validation by performing the validation locally rather than globally in order to detect local errors. Experiments are conducted using the heart phantom ultrasound and human heart ultrasound images. Experimental results show that the VFCCV can generate correct segmentation results in most of the cases, and that the validation step can detect the errors when the contours are inaccurate. Also, the segmentation system is significantly faster than manual validation. For the heart phantom dataset, experiments show that the final ventricle volumes have high accuracy, and the system has high reproducibility.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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