Adrenal Gland Abnormality Detection Using Random Forest Classification.
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...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 5; pp. 891 - 898 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104229506&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104229506 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2013 vid: 26 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104229506 90397224 10.1007/s10278-012-9554-7 NLM23344259 PMC3782594 104229506 ppf: 891 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Adrenal Gland Abnormality Detection Using Random Forest Classification. aug: au: Saiprasad, Ganesh Chang, Chein-I Safdar, Nabile Saenz, Naomi Siegel, Eliot affil: Department of Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle Baltimore 21250 USA sug: subj: Tomography, X-Ray Computed Diagnosis, Computer Assisted Methods Adrenal Gland Diseases Diagnosis Classification Methods Algorithms ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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