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Automatic segmentation of small pulmonary nodules on multidetector-row CT images
International Congress Series 1256 (2003) 1389
Automatic segmentation of small pulmonary nodules on multidetector-row CT images Rie Tachibana a,*, Shoji Kido a, Hayaru Shouno a, Tsuneo Matsumoto b a
Graduate School of Science and Engineering, Yamaguchi University, 2-16-1 Tokiwadai, Ube 755-8611, Japan b Department of Radiopathological Sciences, Yamaguchi University, 1-1-1 Kogushi, Ube 755-8505, Japan Received 14 March 2003; received in revised form 14 March 2003; accepted 18 March 2003
1. Introduction Accurate segmentation of small pulmonary nodules (SPNs) by use of multidetector-row CT (MDCT) images is an important technique for volumetric doubling time estimation and for feature characterization in the diagnosis of SPNs. Our purpose in this study was to develop a threedimensional (3D) automatic segmentation algorithm of SPNs on MDCT images by removing their attached structures, such as vessels and chest walls. 2. Materials and methods Our computerized scheme consists of the following three steps: (1) To set a volume of interest including an SPN on a MDCT image series, using a graphical user interface. (2) A rough segmentation of the SPN; This step has the following two processes: (2a) The decision of threshold with both a fixedthreshold and a k-means algorithm; (2b) The elimination of the chest wall with a template-matching technique. (3) A fine segmentation of the SPN; This step is performed by use of a combination of the following two processes: (3a) The region splitting with multilevel thresholding for the SPN (with CT value); (3b) Making a model from the rough SPN by use of both a distance-transformation and a morphological-opening operation. To evaluate the performance of our algorithm, we selected 31 patients with SPNs. Two chest radiologists evaluated the accuracy of the computerized segmented SPNs on each image (axial, coronal, and sagittal images) by use of a five-level scale of confidence. 3. Results The mean scores of two radiologists for the pooled data were 4.40 F 0.64 (axial), 4.32 F 0.86 (coronal), and 4.61 F 0.55 (sagittal). In the best-score examples (axial: 5; coronal: 5; sagittal: 5), the attached vessels were removed from the SPNs, and the SPNs kept their boundary shape completely. Meanwhile, in the worst-score example (axial: 3; coronal: 3; sagittal: 4), the results of the segmented SPN had an extra region and a defective region compared with those of the radiologists’ evaluation. The extra region was a low-gray-level region that exists around the SPN. 4. Conclusion We have presented a 3D automatic segmentation algorithm of SPNs for estimation of their volumetric changes. As a result of the evaluation of clinical SPNs, radiologists confirmed that our segmentation algorithm is effective for clinical use. * Corresponding author. Tel.: +81-836-85-9500; fax: +81-836-85-9501. E-mail address: [email protected] (R. Tachibana). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00366-2
Automatic segmentation of small pulmonary nodules on multidetector-row CT images Rie Tachibana a,*, Shoji Kido a, Hayaru Shouno a, Tsuneo Matsumoto b a
Graduate School of Science and Engineering, Yamaguchi University, 2-16-1 Tokiwadai, Ube 755-8611, Japan b Department of Radiopathological Sciences, Yamaguchi University, 1-1-1 Kogushi, Ube 755-8505, Japan Received 14 March 2003; received in revised form 14 March 2003; accepted 18 March 2003
1. Introduction Accurate segmentation of small pulmonary nodules (SPNs) by use of multidetector-row CT (MDCT) images is an important technique for volumetric doubling time estimation and for feature characterization in the diagnosis of SPNs. Our purpose in this study was to develop a threedimensional (3D) automatic segmentation algorithm of SPNs on MDCT images by removing their attached structures, such as vessels and chest walls. 2. Materials and methods Our computerized scheme consists of the following three steps: (1) To set a volume of interest including an SPN on a MDCT image series, using a graphical user interface. (2) A rough segmentation of the SPN; This step has the following two processes: (2a) The decision of threshold with both a fixedthreshold and a k-means algorithm; (2b) The elimination of the chest wall with a template-matching technique. (3) A fine segmentation of the SPN; This step is performed by use of a combination of the following two processes: (3a) The region splitting with multilevel thresholding for the SPN (with CT value); (3b) Making a model from the rough SPN by use of both a distance-transformation and a morphological-opening operation. To evaluate the performance of our algorithm, we selected 31 patients with SPNs. Two chest radiologists evaluated the accuracy of the computerized segmented SPNs on each image (axial, coronal, and sagittal images) by use of a five-level scale of confidence. 3. Results The mean scores of two radiologists for the pooled data were 4.40 F 0.64 (axial), 4.32 F 0.86 (coronal), and 4.61 F 0.55 (sagittal). In the best-score examples (axial: 5; coronal: 5; sagittal: 5), the attached vessels were removed from the SPNs, and the SPNs kept their boundary shape completely. Meanwhile, in the worst-score example (axial: 3; coronal: 3; sagittal: 4), the results of the segmented SPN had an extra region and a defective region compared with those of the radiologists’ evaluation. The extra region was a low-gray-level region that exists around the SPN. 4. Conclusion We have presented a 3D automatic segmentation algorithm of SPNs for estimation of their volumetric changes. As a result of the evaluation of clinical SPNs, radiologists confirmed that our segmentation algorithm is effective for clinical use. * Corresponding author. Tel.: +81-836-85-9500; fax: +81-836-85-9501. E-mail address: [email protected] (R. Tachibana). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00366-2