Carpal Bone Segmentation using Modified Multi-Seed based Region Growing
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In the early twenty-first century, minimally invasive surgery is the mainstay of various kinds of surgical fields. Surgeons gave percutaneously surgical treatment of the screw directly using a fluoroscopic view in the past. The latest date, they began to operate the fractured carpal bone surgery using Computerized Tomography (CT). Carpal bones composed of wrist joint consist of eight small bones which have hexahedron and sponge shape. Because of these shape, it is difficult to grasp the shape of carpal bones using only CT image data. Although several image segmentation studies have been conducted with carpal bone CT image data, more studies about carpal bone using CT data are still required. Especially, to apply the software implemented from the studies to clinical field, the outcomes should be user friendly and very accurate. To satisfy those conditions, we propose modified multi-seed region growing segmentation method which uses simple threshold and the canny edge detector for finding edge information more accurately. This method is able to use very easily and gives us high accuracy and high speed for extracting the edge information of carpal bones. Especially, using multi-seed points, multi-bone objects of the carpal bone are extracted simultaneously.
Abstract
In the early twenty-first century, minimally invasive surgery is the mainstay of various kinds of surgical fields. Surgeons gave percutaneously surgical treatment of the screw directly using a fluoroscopic view in the past. The latest date, they began to operate the fractured carpal bone surgery using Computerized Tomography (CT). Carpal bones composed of wrist joint consist of eight small bones which have hexahedron and sponge shape. Because of these shape, it is difficult to grasp the shape of carpal bones using only CT image data. Although several image segmentation studies have been conducted with carpal bone CT image data, more studies about carpal bone using CT data are still required. Especially, to apply the software implemented from the studies to clinical field, the outcomes should be user friendly and very accurate. To satisfy those conditions, we propose modified multi-seed region growing segmentation method which uses simple threshold and the canny edge detector for finding edge information more accurately. This method is able to use very easily and gives us high accuracy and high speed for extracting the edge information of carpal bones. Especially, using multi-seed points, multi-bone objects of the carpal bone are extracted simultaneously.
참고문헌 (11)
- T. Murase, "Does three-dimensional computer simulation improve results of scaphoid nonunion surgery?", (434), pp.143~150 (2005)
- H. Kobatake, "Future CAD in multi-dimensional medical images:Project on multi-organ, multi-disease CAD system", 31(4-5), pp.258~266 (2007)
- F. Gelaude, "Semi-automated segmentation and visualization of outer bone cortex from medical images", 9(1), pp.65~77 (2006)
- D.L. Pham, "A survey of current methods in medical image segmentation", 2, pp.315~337 (1998)
- T.C. Huanga, "Semi-automated CT segmentation using optic flow and Fourier interpolation techniques", 84(2), pp.123~134 (2006)
- J.F Canny, "A computational approach to edge detection", 8, pp.679~698 (1986)
- B.S. Sharif, "Bone Edge Detection in Hand Radiographic Images in Proc. 16th Annual International Conference of the IEEE", pp.514~5
- T.B. Sebastiana, "Segmentation of carpal bones from CT images using skeletally coupled deformable models", 7, pp.2145~ (2003)
- P. Lin, "Carpal-bone feature extraction analysis in skeletal age assessment based on deformable model", 4(3), pp.152~156
- D. Kim, "Convenient semi-automatic segmentation tool", 26(6), pp.407~12 (2005)
- K. Seo, "Automatic liver segmentation of a contrast enhanced CT image using and improved partial histogram threshold algorithm", 26(3), pp.171~6 (2005)