Date of Degree
PhD (Doctor of Philosophy)
Electrical and Computer Engineering
First Committee Member
Christensen, Gary E.
Second Committee Member
Saha, Punam K.
Third Committee Member
Fourth Committee Member
Anderson, Donald D.
A novel method called LOGISMOS - Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces - for simultaneous segmentation of multiple interacting surfaces belonging to multiple interacting objects is reported. The approach is based on representation of the multiple inter-relationships in a single n-dimensional graph, followed by graph optimization that yields a globally optimal solution.
Three major contributions for LOGISMOS are made and illustrated in this thesis: 1) multi-object multi-surface optimal surface detection graph design, 2) implementation of a novel and reliable cross-object surface mapping technique and 3) pattern recognition-based graph cost design.
The LOGISMOS method's utility and performance are demonstrated on a knee joint bone and cartilage segmentation task. Although trained on only a small number of nine example images, this system achieved good performance as judged by Dice Similarity Coefficients (DSC) using a leave-one-out test, with DSC values of 0.84+-0.04, 0.80+-0.04 and 0.80+-0.04 for the femoral, tibial, and patellar cartilage regions, respectively. These are excellent values of DSC considering the narrow-sheet character of the cartilage regions. Similarly, very low signed mean cartilage thickness errors were observed when compared to manually-traced independent standard in 60 randomly selected 3D MR image datasets from the Osteoarthritis Initiative database - 0.11+-0.24, 0.05+-0.23, and 0.03+-0.17 mm for the femoral, tibial, and patellar cartilage thickness, respectively. The average signed surface positioning error for the 6 detected surfaces ranged from 0.04+-0.12 mm to 0.16+-0.22 mm, while the unsigned surface positioning error ranged from 0.22+-0.07 mm to 0.53+-0.14 mm.
The reported LOGISMOS framework provides robust and accurate segmentation of the knee joint bone and cartilage surfaces of the femur, tibia, and patella. As a general segmentation tool, the developed framework can be applied to a broad range of multi-object multi-surface segmentation problems.
Following the LOGISMOS-based cartilage segmentation, a fully automated meniscus segmentation system was build using pattern recognition technique. The leave-one-out test for the nine training images showed very good mean DSC 0.80+-0.04. The signed and unsigned surface positioning error when compared to manually-traced independent standard in the 60 randomly selected 3D MR image datasets is 0.65+-0.20 and 0.68+-0.20 mm respectively.
x, 110 pages
Includes bibliographical references (pages 100-110).
Copyright 2010 Yin Yin
Yin, Yin. "Multi-surface, multi-object optimal image segmentation: application in 3D knee joint imaged by MR." PhD (Doctor of Philosophy) thesis, University of Iowa, 2010.