Document Detail


Adrenal Gland Abnormality Detection Using Random Forest Classification.
MedLine Citation:
PMID:  23344259     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Adrenal abnormalities are commonly identified on computed tomography (CT) and are seen in at least 5 % of CT examinations of the thorax and abdomen. Previous studies have suggested that evaluation of Hounsfield units within a region of interest or a histogram analysis of a region of interest can be used to determine the likelihood that an adrenal gland is abnormal. However, the selection of a region of interest can be arbitrary and operator dependent. We hypothesize that segmenting the entire adrenal gland automatically without any human intervention and then performing a histogram analysis can accurately detect adrenal abnormality. We use the random forest classification framework to automatically perform a pixel-wise classification of an entire CT volume (abdomen and pelvis) into three classes namely right adrenal, left adrenal, and background. Once we obtain this classification, we perform histogram analysis to detect adrenal abnormality. The combination of these methods resulted in a sensitivity and specificity of 80 and 90 %, respectively, when analyzing 20 adrenal glands seen on volumetric CT datasets for abnormality.
Authors:
Ganesh Saiprasad; Chein-I Chang; Nabile Safdar; Naomi Saenz; Eliot Siegel
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-24
Journal Detail:
Title:  Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology     Volume:  -     ISSN:  1618-727X     ISO Abbreviation:  J Digit Imaging     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9100529     Medline TA:  J Digit Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA, ganeshs1@umbc.edu.
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