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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Bibliographic Details
Published in:BioMed research international 2016-01, Vol.2016 (2016), p.1-15
Main Authors: Arcay, Bernardino, Boveda, Carmen, Rey, Alberto, Castro, Alfonso, Sanjurjo, Pedro
Format: Article
Language:English
Subjects:
Algorithms
Biomedical research
Classification
Clustering
CT imaging
Diagnostic imaging
Fuzzy Logic
Humans
Lung Neoplasms - diagnostic imaging
Lungs
Medical imaging
Medical imaging equipment
Morphology
Mortality
Neighborhoods
Pattern Recognition, Automated - methods
Radiography, Thoracic - methods
Reproducibility of Results
Sensitivity and Specificity
Social aspects
Solitary Pulmonary Nodule - diagnostic imaging
Tomography, Spiral Computed - methods
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Summary: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).
ISSN:2314-6133
2314-6141
DOI:10.1155/2016/8058245