Object-based deformation technique for 3D CT lung nodule detection

Shyhliang A. Lou; Chun-Long Chang; Kang-Ping Lin; Te-Shin Chen

doi:10.1117/12.348557

21 May 1999 Object-based deformation technique for 3D CT lung nodule detection

Shyhliang A. Lou, Chun-Long Chang, Kang-Ping Lin, Te-Shin Chen

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348557
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

Helical CT scans have shown effectiveness in detecting lung nodules compared with the convention thoracic radiography. However, in a two-dimensional (2-D) image slice, it is difficult to differentiate nodules from the vertically oriented pulmonary blood vessels. This paper reports an object-based deformation method to detect lung nodules from CT images in three-dimension (3-D). Object-based deformation method in this paper consists of preprocessing and nodule detection. CT numbers are used to identify the pulmonary region and the objects of nodules, blood vessels, and airways. Hough transform is used to identify each circle shape within the pulmonary region. The circles in the different slices are then grouped into the same nodule, airway, or blood to be a target object. To differentiate lung nodules from blood vessels and airways, we use a deformable seed object technique. For a given target object within the pulmonary region, the seed object grows within the target object until it is against the wall of the target object. The seed object is then deformed to match the target object. A cost function is used to match the seed object and the target object. Eight patient cases with 18 nodules were included in this study and the average size of the nodules was 2.4 cm approximately.

Citation Download Citation

Shyhliang A. Lou, Chun-Long Chang, Kang-Ping Lin, and Te-Shin Chen "Object-based deformation technique for 3D CT lung nodule detection", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348557

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