Segmentation of carpal bones from CT images using skeletally coupled deformable models

Sebastian, Thomas; Tek, Hüseyin; Crisco, Joseph J.; Kimia, Benjamin B.

doi:10.1016/s1361-8415(02)00065-8

Cited by 111 publications

(77 citation statements)

References 33 publications

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“…Each of the seven CT hand-posture images was segmented in order to generate digital surface representations of all the bones in the posture. The segmentation was completed by manually selecting the pixels representing the bone surfaces in each slice of each image after initially applying global thresholding [21]. The collection of digital bone surfaces for each posture was then used to determine joint flexion angles for the same finger and thumb joints we examined with the Cyberglove.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

Buffi

Sancho-Bru

Crisco³

et al. 2014

Journal of Biomechanical Engineering

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There has been a marked increase in the use of hand motion capture protocols in the past 20 yr. However, their absolute accuracies and precisions remain unclear. The purpose of this technical brief was to present a method for evaluating the accuracy and precision of the joint angles determined by a hand motion capture protocol using simultaneously collected static computed tomography (CT) images. The method consists of: (i) recording seven functional postures using both the motion capture protocol and a CT scanner; (ii) obtaining principal axes of the bones in each method; (iii) calculating the flexion angle at each joint for each method as the roll angle of the composite, sequential, roll-pitchyaw rotations relating the orientation of the distal bone to the proximal bone; and (iv) comparing corresponding joint angle measurements. For demonstration, we applied the method to a Cyberglove protocol. Accuracy and precision of the instrumentedglove protocol were calculated as the mean and standard deviation, respectively, of the differences between the angles determined from the Cyberglove output and the CT images across the seven postures. Implementation in one subject highlighted substantial errors, especially for the distal joints of the fingers. This technical note both clearly demonstrates the need for future work and introduces a solid, technical approach with the potential to improve the current state of such assessments in our field.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

Buffi

Sancho-Bru

Crisco³

et al. 2014

Journal of Biomechanical Engineering

Self Cite

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“…The methods in Refs. 19,20, and 23 employ slice-by-slice strategies, as such they demand a significant amount of user time, even if it is just for ascertaining that the segmented results are acceptable in every slice. Reyes-Aldasoro et al in Ref.…”

Section: Ib Previous Workmentioning

confidence: 99%

Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis

et al. 2008

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There are several medical application areas that require the segmentation and separation of the component bones of joints in a sequence of images of the joint acquired under various loading conditions, our own target area being joint motion analysis. This is a challenging problem due to the proximity of bones at the joint, partial volume effects, and other imaging modality-specific factors that confound boundary contrast. In this article, a two-step model-based segmentation strategy is proposed that utilizes the unique context of the current application wherein the shape of each individual bone is preserved in all scans of a particular joint while the spatial arrangement of the bones alters significantly among bones and scans. In the first step, a rigid deterministic model of the bone is generated from a segmentation of the bone in the image corresponding to one position of the joint by using the live wire method. Subsequently, in other images of the same joint, this model is used to search for the same bone by minimizing an energy function that utilizes both boundaryand region-based information. An evaluation of the method by utilizing a total of 60 data sets on MR and CT images of the ankle complex and cervical spine indicates that the segmentations agree very closely with the live wire segmentations, yielding true positive and false positive volume fractions in the range 89%-97% and 0.2%-0.7%. The method requires 1 -2 minutes of operator time and 6 -7 min of computer time per data set, which makes it significantly more efficient than live wire-the method currently available for the task that can be used routinely.

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“…8,9 Most of the methods have shown success in certain anatomical structures where they have been optimized, such as carpal bones, 9 acetabulum and femoral head, 10 spinal canal, 11 pelvis, 7,12 vertebrae, 13 ribs, 14 and phalanx bones. 15 In, 8 two methods were validated on knee bone segmentation, which is also the subject of this study.…”

Section: Introductionmentioning

confidence: 99%

“…Although the integration of edge and region information made the model more robust to noise and permitted a more precise segmentation of bones, the automatic selection of relative weighting between edge and region terms remains an unsolved problem. In another work, 9 Sebastian et al tional approaches, such as bubble ACs, 20 region growing, region competition, and morphological operations, in a unified framework. Specifically, from initialized seeds, region growing took place under the evolution implementation of bubbles.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Feasibility of Active Contours on Automatic CT Bone Segmentation

et al. 2009

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Automatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active contour (AC) approaches, but none claim fully satisfactory performance. Recently, geometric or level-set-based AC models have been developed and appear to have characteristics suitable for automatic bone segmentation such as initialization insensitivity and topology adaptability. In this study, we have tested the feasibility of five level-set-based AC approaches for automatic CT bone segmentation with both synthetic and real CT images: namely, the geometric AC, geodesic AC, gradient vector flow fast geometric AC, Chan-Vese (CV) AC, and our proposed density distance augmented CV AC (Aug. CV AC). Qualitative and quantitative evaluations have been made in comparison with the segmentation results from standard commercial software and a medical expert. The first three models showed their robustness to various image contrasts, but their performances decreased much when noise level increased. On the contrary, the CV AC's performance was more robust to noise, yet dependent on image contrast. On the other hand, the Aug. CV AC demonstrated its robustness to both noise and contrast levels and yielded improved performances on a set of real CT data compared with the commercial software, proving its suitability for automatic bone segmentation from CT images.

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Segmentation of carpal bones from CT images using skeletally coupled deformable models

Cited by 111 publications

References 33 publications

Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis

A Study on the Feasibility of Active Contours on Automatic CT Bone Segmentation

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