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Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants.

The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing...

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Published in:BioMed Research International Vol. 2016; pp. 1 - 16
Main Authors: Castro, Alfonso, Rey, Alberto, Boveda, Carmen, Arcay, Bernardino, Sanjurjo, Pedro
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 7/18/2016
Online Access:View this record in EBSCOhost
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      jtl: BioMed Research International
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      dt: 7/18/2016
      vid: 2016
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        116872668
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        10.1155/2016/8058245
        116872668
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        atl: Fuzzy Clustering Applied to ROI Detection in Helical Thoracic CT Scans with a New Proposal and Variants.
      aug:
        au:
          Castro, Alfonso
          Rey, Alberto
          Boveda, Carmen
          Arcay, Bernardino
          Sanjurjo, Pedro
        affil: Department of Information and Communication Technologies, Faculty of Computer Science, University of A Coruna, Campus de A Coruña, 15071 A Coruña, Spain
      sug:
        subj:
          Solitary Pulmonary Nodule Diagnosis
          Algorithms
          Tomography, Spiral Computed
          Diagnosis, Computer Assisted
          Resource Databases
          Human
          United States
          ROC Curve
          Descriptive Statistics
          Data Analysis Software
          Funding Source
      ab: The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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