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30 March 2007 A new method for detecting colonic polyps based on local intensity structure analysis from 3D abdominal CT images
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Abstract
This paper presents a new method for detecting colonic polyps from abdominal CT images based on Hessian matrix analysis. Recently, virtual colonoscopy (VC) has widely received attention as a new and less-invasive colon diagnostic method. A physician diagnoses the inside of the colon using a virtual colonoscopy system. However, since the colon has many haustra and its shape is long and convoluted, a physician has to change viewpoints and viewing directions of the virtual camera many times while diagnosing. Lesions behind haustra may be overlooked. Thus this paper proposes an automated colonic polyp detection method from 3D abdominal CT images. Colonic polyps are located on the colonic wall, and their CT values are higher than colonic lumen regions. In addition, CT values inside polyps tend to gradually increase from outward to inward (blob-like structure). We employ a blob structure enhancement filter based on the eigenvalues of a Hessian matrix to detect polyps with the above blob-shaped characteristics. For reducing FPs, we eliminate polyp candidate regions in which the maximum output value of the blob structure enhancement filter is smaller than given threshold values. Also small regions are removed from candidates. We applied the proposed method to 23 cases of abdominal CT images. Overall, 74.4% of the polyps were detected with 3.8 FPs per case.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Masahiro Oda, Yuichi Nakada, Takayuki Kitasaka, Kensaku Mori, Yasuhito Suenaga, Tetsuji Takayama, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, and Shigeru Nawano "A new method for detecting colonic polyps based on local intensity structure analysis from 3D abdominal CT images", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65142L (30 March 2007); https://doi.org/10.1117/12.709153
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