Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound.
Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of r...
| Published in: | Journal of Digital Imaging Vol. 26; no. 6; pp. 1058 - 1071 |
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| Main Authors: | , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Dec2013
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104153854&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104153854 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2013 vid: 26 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104153854 91842817 10.1007/s10278-013-9578-7 NLM23412917 104153854 ppf: 1058 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound. aug: au: Virmani, Jitendra Kumar, Vinod Kalra, Naveen Khandelwal, Niranjan affil: Biomedical Instrumentation Laboratory, Department of Electrical Engineering, Indian Institute of Technology Roorkee, Uttarakhand 247667 India sug: subj: Liver Neoplasms Diagnosis Liver Neoplasms Classification Liver Neoplasms Ultrasonography Human Radiology Service Methods Algorithms Liver Neoplasms Radiography ab: Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of regions inside the lesion and the texture of background liver on which the lesion has evolved. The present study investigates the contribution made by texture patterns of regions inside and outside of the lesions for binary classification between HCC and MET lesions. The study is performed on 51 real ultrasound liver images with 54 malignant lesions, i.e., 27 images with 27 solitary HCCs (13 small HCCs and 14 large HCCs) and 24 images with 27 MET lesions (12 typical cases and 15 atypical cases). A total of 120 within-lesion regions of interest and 54 surrounding lesion regions of interest are cropped from 54 lesions. Subsequently, 112 texture features (56 texture features and 56 texture ratio features) are computed by statistical, spectral, and spatial filtering based texture features extraction methods. A two-step methodology is used for feature set optimization, i.e., feature pruning by removal of nondiscriminatory features followed by feature selection by genetic algorithm-support vector machine (SVM) approach. The SVM classifier is designed based on optimum features. The proposed computer-aided diagnostic system achieved the overall classification accuracy of 91.6 % with sensitivity of 90 % and 93.3 % for HCCs and METs, respectively. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in diagnosing liver malignancies. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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