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KMID : 0603720160220040305
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Journal of Korean Society of Medical Informatics
2016 Volume.22 No. 4 p.305 ~ p.315
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Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules onThin-Section CT Scans
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Kim Young-Jae
Lee Seung-Hyun
Park Chang-Min
Kim Kwang-Gi
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Abstract
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Objectives: This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them.
Methods: To do this,we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seeded region growing, K-means clustering, and fuzzy C-means clustering) to preprocessed GGN images, respectively. Then, to measure the similarities, we calculated the Dice coefficient of the segmented areausing each semiautomatic method with the result of the manually segmented area by two radiologists.
Results: Comparison experiments were performed using 40 persistent GGNs. In our experiment, the mean Dice coefficient for each semiautomatic segmentation tool with manually segmented area was 0.808 for the level-set-based active contour model, 0.8001 for the localized region-based active contour model, 0.629 for seeded region growing, 0.7953 for K-means clustering, and 0.7999 for fuzzy C-means clustering, respectively.
Conclusions: The level-set-based active contour model algorithm showed the best performance, which was most similar to the result of manual segmentation by two radiologists. From the differentiation between the normal parenchyma and the nodule, it was also the most efficient. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for more accurate early diagnosis and prognosis of lung cancer inthin-section CT images.
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KEYWORD
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Lung, Computer-Assisted Image Processing, Solitary Pulmonary Nodule, Computer-Assisted Diagnosis, X-Ray Computed Tomography Scanners
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