TY - GEN
T1 - Optimizing lung volume segmentation by texture classification
AU - Korfiatis, Panayiotis
AU - Kazantzi, Alexandra
AU - Kalogeropoulou, Christina
AU - Petsas, Theodoros
AU - Costaridou, Lena
PY - 2010
Y1 - 2010
N2 - Accurate and automated Lung Field (LF) segmentation in volumetric computed tomography protocols is highly challenged by the presence of pathologies affecting lung borders, also affecting the performance of computer-aided diagnosis (CAD) schemes. In this work, a three-dimensional LF segmentation algorithm adapted to interstitial lung disease patterns (ILD) patterns is presented. The algorithm employs kmeans clustering followed by a filling operation to obtain an initial LF order estimate. The final LF border is obtained by an iterative support vector machine neighborhood labeling of border pixels based on 3D texture features. The proposed method is evaluated on a dataset of 10 cases spanning a range of ILD patterns such as ground glass, reticular, and honeycombing. The accuracy of the method is assessed using area overlap and shape differentiation metrics (dmean, drms', and dmax), by comparing automatically derived lung borders to manually traced ones by a radiologist, and further compared to a Gray Level Thresholding-based (GLT-based) method. The proposed method demonstrated the highest segmentation accuracy, overlap=0.942, dmean=1.835 mm, d rms=1.672 mm, and dmax =4.255 mm, which is statistically significant (two-tailed student's t test for paired data, p<0.0001) with respect to all metrics considered as compared to the the GLT-based method overlap=0.836, dmean=2.324 mm, drms=3.890 mm,and d max=2.946 mm. The proposed segmentation method could be used as an initial stage of a CAD scheme for ILD patterns.
AB - Accurate and automated Lung Field (LF) segmentation in volumetric computed tomography protocols is highly challenged by the presence of pathologies affecting lung borders, also affecting the performance of computer-aided diagnosis (CAD) schemes. In this work, a three-dimensional LF segmentation algorithm adapted to interstitial lung disease patterns (ILD) patterns is presented. The algorithm employs kmeans clustering followed by a filling operation to obtain an initial LF order estimate. The final LF border is obtained by an iterative support vector machine neighborhood labeling of border pixels based on 3D texture features. The proposed method is evaluated on a dataset of 10 cases spanning a range of ILD patterns such as ground glass, reticular, and honeycombing. The accuracy of the method is assessed using area overlap and shape differentiation metrics (dmean, drms', and dmax), by comparing automatically derived lung borders to manually traced ones by a radiologist, and further compared to a Gray Level Thresholding-based (GLT-based) method. The proposed method demonstrated the highest segmentation accuracy, overlap=0.942, dmean=1.835 mm, d rms=1.672 mm, and dmax =4.255 mm, which is statistically significant (two-tailed student's t test for paired data, p<0.0001) with respect to all metrics considered as compared to the the GLT-based method overlap=0.836, dmean=2.324 mm, drms=3.890 mm,and d max=2.946 mm. The proposed segmentation method could be used as an initial stage of a CAD scheme for ILD patterns.
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U2 - 10.1109/ITAB.2010.5687763
DO - 10.1109/ITAB.2010.5687763
M3 - Conference contribution
AN - SCOPUS:79951654715
SN - 9781424465606
T3 - Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB
BT - ITAB 2010 - 10th International Conference on Information Technology and Applications in Biomedicine
T2 - 10th International Conference on Information Technology and Applications in Biomedicine: Emerging Technologies for Patient Specific Healthcare, ITAB 2010
Y2 - 2 November 2010 through 5 November 2010
ER -