Automated segmentations of skin, soft-tissue, and skeleton, from torso CT images

Xiangrong Zhou; Takeshi Hara; Hiroshi Fujita; Ryujiro Yokoyama; Takuji Kiryu; Hiroaki Hoshi

doi:10.1117/12.534843

12 May 2004 Automated segmentations of skin, soft-tissue, and skeleton, from torso CT images

Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita, Ryujiro Yokoyama, Takuji Kiryu, Hiroaki Hoshi

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.534843
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

We have been developing a computer-aided diagnosis (CAD) scheme for automatically recognizing human tissue and organ regions from high-resolution torso CT images. We show some initial results for extracting skin, soft-tissue and skeleton regions. 139 patient cases of torso CT images (male 92, female 47; age: 12-88) were used in this study. Each case was imaged with a common protocol (120kV/320mA) and covered the whole torso with isotopic spatial resolution of about 0.63 mm and density resolution of 12 bits. A gray-level thresholding based procedure was applied to separate the human body from background. The density and distance features to body surface were used to determine the skin, and separate soft-tissue from the others. A 3-D region growing based method was used to extract the skeleton. We applied this system to the 139 cases and found that the skin, soft-tissue and skeleton regions were recognized correctly for 93% of the patient cases. The accuracy of segmentation results was acceptable by evaluating the results slice by slice. This scheme will be included in CAD systems for detecting and diagnosing the abnormal lesions in multi-slice torso CT images.

Citation Download Citation

Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita, Ryujiro Yokoyama, Takuji Kiryu, and Hiroaki Hoshi "Automated segmentations of skin, soft-tissue, and skeleton, from torso CT images", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.534843

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