TY - JOUR
T1 - Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT
AU - Korfiatis, P.
AU - Kalogeropoulou, C.
AU - Daoussis, D.
AU - Petsas, T.
AU - Adonopoulos, A.
AU - Costaridou, L.
PY - 2009
Y1 - 2009
N2 - Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP
AB - Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP
KW - Computerized Tomography (CT) and Computed Radiography (CR)
KW - Data processing methods
KW - Pattern recognition, cluster finding, calibration and fitting methods
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U2 - 10.1088/1748-0221/4/07/P07013
DO - 10.1088/1748-0221/4/07/P07013
M3 - Article
AN - SCOPUS:71049188490
VL - 4
JO - Journal of Instrumentation
JF - Journal of Instrumentation
SN - 1748-0221
IS - 7
M1 - P07013
ER -