Jianan Wei1, Huawei Cai2, Yong Pi1, Zhen Zhao3, Zhang Yi4. 1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China. 2. Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041, People's Republic of China. 3. Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041, People's Republic of China. zhaozhen1982@126.com. 4. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People's Republic of China. zhangyi@scu.edu.cn.
Abstract
PURPOSE: Whole-body bone scintigraphy (WBS) is one of the common imaging methods in nuclear medicine. It is a time-consuming, tedious, and error-prone issue for physicians to determine the location of bone lesions which is important for the qualitative diagnosis of bone lesions. In this paper, an automatic fine-grained skeleton segmentation method for WBS is developed. METHOD: The proposed method contains four steps. In the first step, a novel denoising method is proposed to remove the noise from WBS which benefits the location of the skeleton. In the second step, a restoration method based on gray probability distribution is developed to repair the partial contamination caused by the high local density of radionuclide. Then, the standardization for WBS is performed by the exact histogram matching. Finally, the deformation field between the atlas and the input WBS is calculated by registration, and the segmentation mask of the input WBS is obtained by wrapping the segmentation mask of the atlas with the deformation field. RESULTS: The experimental results show that the proposed method outperforms the traditional registration (Morphon): mean square error decreased from [Formula: see text] to [Formula: see text], peak signal-to-noise ratio increased from 21.26 to 26.92, and mean structural similarity increased from 0.9986 to 0.9998. CONCLUSIONS: Our experiments show that the proposed method can achieve robust and fine-grained results which outperform the traditional registration method, indicating it could be helpful in clinical application. To the best of our knowledge, this is the first work that implements a fully automated fine-grained skeleton segmentation method for WBS.
PURPOSE: Whole-body bone scintigraphy (WBS) is one of the common imaging methods in nuclear medicine. It is a time-consuming, tedious, and error-prone issue for physicians to determine the location of bone lesions which is important for the qualitative diagnosis of bone lesions. In this paper, an automatic fine-grained skeleton segmentation method for WBS is developed. METHOD: The proposed method contains four steps. In the first step, a novel denoising method is proposed to remove the noise from WBS which benefits the location of the skeleton. In the second step, a restoration method based on gray probability distribution is developed to repair the partial contamination caused by the high local density of radionuclide. Then, the standardization for WBS is performed by the exact histogram matching. Finally, the deformation field between the atlas and the input WBS is calculated by registration, and the segmentation mask of the input WBS is obtained by wrapping the segmentation mask of the atlas with the deformation field. RESULTS: The experimental results show that the proposed method outperforms the traditional registration (Morphon): mean square error decreased from [Formula: see text] to [Formula: see text], peak signal-to-noise ratio increased from 21.26 to 26.92, and mean structural similarity increased from 0.9986 to 0.9998. CONCLUSIONS: Our experiments show that the proposed method can achieve robust and fine-grained results which outperform the traditional registration method, indicating it could be helpful in clinical application. To the best of our knowledge, this is the first work that implements a fully automated fine-grained skeleton segmentation method for WBS.
Authors: Charito Love; Anabella S Din; Maria B Tomas; Tomy P Kalapparambath; Christopher J Palestro Journal: Radiographics Date: 2003 Mar-Apr Impact factor: 5.333
Authors: May Sadik; Iman Hamadeh; Pierre Nordblom; Madis Suurkula; Peter Höglund; Mattias Ohlsson; Lars Edenbrandt Journal: J Nucl Med Date: 2008-11-07 Impact factor: 10.057