A CSC Based Classification Method for CT Bone Images

Sturm, Patrick; Priese, Lutz; Wang, Haojun

doi:10.1109/3dpvt.2006.11

Cited by 4 publications

(3 citation statements)

References 10 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No cardiac nor respiratory gating was used. The data was subsampled to 144 3 μm 3 voxelsize (≈ 250 × 200 × 650 voxels), smoothed with a Gaussian filter (σ = 1) and segmented using the Color Structure Code technique [13] with T = 24 for the skeleton and the skin and T = 6 for the lungs. Triangular surface meshes were extracted from the segmentations using Marching Cubes (more details in [1]).…”

Section: Methodsmentioning

confidence: 99%

Automated Registration of Whole-Body Follow-Up MicroCT Data of Mice

Baiker

Staring

Löwik

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In vivo MicroCT imaging of disease models at multiple time points is of great importance for preclinical oncological research, to monitor disease progression. However, the great postural variability between animals in the imaging device complicates data comparison.In this paper we propose a method for automated registration of whole-body MicroCT follow-up datasets of mice. First, we register the skeleton, the lungs and the skin of an articulated animal atlas (Segars et al. 2004) to MicroCT datasets, yielding point correspondence of these structures over all time points. This correspondence is then used to regularize an intensity-based B-spline registration. This two step approach combines the robustness of model-based registration with the high accuracy of intensity-based registration.We demonstrate our approach using challenging whole-body in vivo follow-up MicroCT data and obtain subvoxel accuracy for the skeleton and the skin, based on the Euclidean surface distance. The method is computationally efficient and enables high resolution whole-body registration in ≈17 minutes with unoptimized code, mostly executed single-threaded.

show abstract

Section: Methodsmentioning

confidence: 99%

Automated Registration of Whole-Body Follow-Up MicroCT Data of Mice

Baiker

Staring

Löwik

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…One such paper [1] uses Color Structure Codes (CSC) to segment bones from CT images. Another paper by Sonka et.…”

Section: Introductionmentioning

confidence: 99%

Exploiting typical clinical imaging constraints for 3D outer bone surface segmentation

Mack

Mogallapu

Willis

et al. 2007

Proceedings 2007 IEEE SoutheastCon

View full text Add to dashboard Cite

show abstract

“…CT Bone Classifier The second use case is the classification of CT images [9] into the classes background, soft tissue, enclosed air and bones. Especially the automatic classification of bones from CT images is an important task in medical diagnostic, surgery planning and visualization.…”

Section: 2mentioning

confidence: 99%