Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm.
Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implement...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 3; pp. 1 - 19 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2015
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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=115925409&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925409 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2015 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925409 115925409 115925409 10.1007/s10916-015-0214-6 115925409 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm. aug: au: Noor, Norliza Than, Joel Rijal, Omar Kassim, Rosminah Yunus, Ashari Zeki, Amir Anzidei, Michele Saba, Luca Suri, Jasjit affil: Department of Engineering, UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur Malaysia sug: subj: Lung Diseases, Interstitial Radiography Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Systems Design Human Funding Source Lung Radiography Retrospective Design Malaysia Male Female Descriptive Statistics Pilot Studies Lung Diseases, Interstitial Classification Reliability and Validity Male Female ab: Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52 % for Jaccard Index and 98.21 % for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), −1.15 % for Relative Area Error and 4.09 % Area Overlap Error. The right lung's performance of segmentation was 97.24 % for Jaccard Index, 98.58 % for Dice Similarity, 0.61 mm for PDM, −0.03 % for Relative Area Error and 3.53 % for Area Overlap Error. The segmentation overall has an overall similarity of 98.4 %. The segmentation proposed is an accurate and fully automated system. 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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