17 March 2008 Computer-aided diagnosis: a 3D segmentation method for lung nodules in CT images by use of a spiral-scanning technique
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Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69151H (2008); doi: 10.1117/12.771455
Event: Medical Imaging, 2008, San Diego, California, United States
Abstract
Lung nodule segmentation in computed tomography (CT) plays an important role in computer-aided detection, diagnosis, and quantification systems for lung cancer. In this study, we developed a simple but accurate nodule segmentation method in three-dimensional (3D) CT. First, a volume of interest (VOI) was determined at the location of a nodule. We then transformed the VOI into a two-dimensional (2D) image by use of a "spiral-scanning" technique, in which a radial line originating from the center of the VOI spirally scanned the VOI. The voxels scanned by the radial line were arranged sequentially to form a transformed 2D image. Because the surface of a nodule in 3D image became a curve in the transformed 2D image, the spiral-scanning technique considerably simplified our segmentation method and enabled us to obtain accurate segmentation results. We employed a dynamic programming technique to delineate the "optimal" outline of a nodule in the 2D image, which was transformed back into the 3D image space to provide the interior of the nodule. The proposed segmentation method was trained on the first and was tested on the second Lung Image Database Consortium (LIDC) datasets. An overlap between nodule regions provided by computer and by the radiologists was employed as a performance metric. The experimental results on the LIDC database demonstrated that our segmentation method provided relatively robust and accurate segmentation results with mean overlap values of 66% and 64% for the nodules in the first and second LIDC datasets, respectively, and would be useful for the quantification, detection, and diagnosis of lung cancer.
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Jiahui Wang, Roger Engelmann, Qiang Li, "Computer-aided diagnosis: a 3D segmentation method for lung nodules in CT images by use of a spiral-scanning technique", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69151H (17 March 2008); doi: 10.1117/12.771455; https://doi.org/10.1117/12.771455
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KEYWORDS
Image segmentation

3D image processing

Computed tomography

Lung

Computer programming

Databases

Lung cancer

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