Paper Info
Title
Obtaining The Potential Number Of Object Models/Atlases Needed In Medical Image Analysis
Abstract
Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.
Year
DOI
Venue
2020
10.1117/12.2549827
MEDICAL IMAGING 2020: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING
Keywords
DocType
Volume
model/ atlas-based methods, image grouping, head and neck CT, hierarchical agglomerative clustering
Conference
11315
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ze Jin100.34
Jayaram K. Udupa22481322.29
D. A. Torigian38121.68