DocumentCode
2568862
Title
Emphysema quantification in a multi-scanner HRCT cohort using local intensity distributions
Author
Mendoza, C.S. ; Washko, G.R. ; Ross, J.C. ; Diaz, A.A. ; Lynch, D.A. ; Crapo, J.D. ; Silverman, E.K. ; Acha, B. ; Serrano, C. ; Estépar, R. San José
Author_Institution
Univ. de Sevilla, Sevilla, Spain
fYear
2012
fDate
2-5 May 2012
Firstpage
474
Lastpage
477
Abstract
This article investigates the suitability of local intensity distributions to analyze six emphysema classes in 342 CT scans obtained from 16 sites hosting scanners by 3 vendors and a total of 9 specific models in subjects with Chronic Obstructive Pulmonary Disease (COPD). We propose using kernel density estimation to deal with the inherent sparsity of local intensity histograms obtained from scarcely populated regions of interest. We validate our approach by leave-one-subject-out classification experiments and full-lung analyses. We compare our results with recently published LBP texture-based methodology. We demonstrate the efficacy of using intensity information alone in multi-scanner cohorts, which is a simpler, more intuitive approach.
Keywords
computerised tomography; diseases; estimation theory; image classification; image texture; lung; medical image processing; chronic obstructive pulmonary disease; computerised tomography; emphysema quantification; full-lung analysis; inherent sparsity; kernel density estimation; leave-one-subject-out classification experiments; local intensity distributions; local intensity histograms; multiscanner cohorts; published LBP texture-based methodology; scarcely populated regions-of-interest; Computed tomography; Diseases; Estimation; Histograms; Kernel; Lungs; Radiology; COPD; Densitometry; Emphysema; Texture analysis; Tissue classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on
Conference_Location
Barcelona
ISSN
1945-7928
Print_ISBN
978-1-4577-1857-1
Type
conf
DOI
10.1109/ISBI.2012.6235587
Filename
6235587
Link To Document