Paper Info
Title
Image quality and segmentation.
Abstract
Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.
Year
DOI
Venue
2018
10.1117/12.2293622
Proceedings of SPIE
Keywords
DocType
Volume
Image quality,image segmentation,segmentation evaluation
Conference
10576
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
1
8
Name
Order
Citations
PageRank
Gargi Pednekar122.14
Jayaram K. Udupa22481322.29
David J. McLaughlin300.34
Xingyu Wu485.68
Yubing Tong59322.73
Charles B. Simone II632.62
Joseph Camaratta721.46
D. A. Torigian88121.68