Segmentation of bone CT images and assessment of bone structure using measures of complexity
Segmentation of bone CT images and assessment of bone structure using measures of complexity
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DOI：
10.1118/1.2336501
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发表时间：
2006-10-01
期刊：
影响因子：
3.8
通讯作者：
Gowin, Wolfgang
中科院分区：
文献类型：
--
作者：
Saparin, Peter;Thomsen, Jesper Skovhus;Gowin, Wolfgang
一种用于人体骨组织结构组成的非破坏性和非侵入性的数值表征（定量）方法已经开发和测试。为了量化和比较从不同骨骼位置获得的二维计算机断层扫描（CT）图像中骨骼的结构组成，开发了一系列鲁棒、通用和可调的图像分割和结构评估算法。分割技术有利于骨小梁与皮质骨的分离，并使感兴趣的区域标准化。分割的图像是符号编码和骨结构组成的不同方面量化使用六种不同的复杂性措施。这些结构检查是在桡骨远端、肱骨中段、椎体、股骨头、股骨颈、胫骨近端和跟骨的骨标本的CT图像上进行的。此外，通过将复杂性的非侵入性和非破坏性测量方法与对人体第四腰椎进行的传统静态组织形态测量法进行比较，验证了其量化小梁骨结构的能力。除了表达小梁厚度的测量外，复杂性测量与组织形态学参数之间建立了强相关性。此外，通过比较CT图像的复杂性分析结果与第三腰椎椎体的生物力学压缩测试结果，研究了复杂性测量预测椎体骨强度的能力，这些结果来自于用于组织形态学测量的同一人群。采用结构复杂性指数、结构无序指数、小梁网络指数、全局集合指数、最大l块和x射线衰减分布熵等指标进行多元回归分析，发现复杂性指标与骨抗压强度之间存在良好的关系（r = 0.959, r(2) = 0.92）。总之，图像分割技术和骨结构的复杂性评估已成功应用于分析高分辨率外围定量计算机断层扫描（pQCT）和CT图像，这些图像来自桡骨远端、肱骨中段、第三和第四腰椎、股骨近端、胫骨近端和跟骨。所提出的方法具有广泛的意义，因为它可以应用于其他科学领域中源自不同成像模式的结构和纹理的量化。(c) 2006年美国医学物理学家协会。
A nondestructive and noninvasive method for numeric characterization (quantification) of the structural composition of human bone tissue has been developed and tested. In order to quantify and to compare the structural composition of bones from 2D computed tomography (CT) images acquired at different skeletal locations, a series of robust, versatile, and adjustable image segmentation and structure assessment algorithms were developed. The segmentation technique facilitates separation of trabecular from cortical bone and standardizes the region of interest. The segmented images were symbol-encoded and different aspects of the bone structural composition were quantified using six different measures of complexity. These structural examinations were performed on CT images of bone specimens obtained at the distal radius, humeral mid-diaphysis, vertebral body, femoral head, femoral neck, proximal tibia, and calcaneus. In addition, the ability of the noninvasive and nondestructive measures of complexity to quantify trabecular bone structure was verified by comparing them to conventional static histomorphometry performed on human fourth lumbar vertebral bodies. Strong correlations were established between the measures of complexity and the histomorphometric parameters except for measures expressing trabecular thickness. Furthermore, the ability of the measures of complexity to predict vertebral bone strength was investigated by comparing the outcome of the complexity analysis of the CT images with the results of a biomechanical compression test of the third lumbar vertebral bodies from the same population as used for histomorphometry. A multiple regression analysis using the proposed measures including structure complexity index, structure disorder index, trabecular network index, index of a global ensemble, maximal L-block, and entropy of x-ray attenuation distribution revealed an excellent relationship (r = 0.959, r(2) = 0.92) between the measures of complexity and compressive bone strength. In conclusion, the image segmentation techniques and the assessment of bone architecture by measures of complexity have been successfully applied to analyze high-resolution peripheral quantitative computed tomography (pQCT) and CT images obtained from the distal radius, humeral mid-diaphysis, third and fourth lumbar vertebral bodies, proximal femur, proximal tibia, and calcaneus. The proposed approach is of broad interest as it can be applied for the quantification of structures and textures originating from different imaging modalities in other fields of science. (c) 2006 American Association of Physicists in Medicine.