Bone segmentation and 3D visualization of CT images for traumatic pelvic injuries

Wu, Jie; Belle, Ashwin; Hargraves, Rosalyn Hobson; Cockrell, Charles; Tang, Yang; Najarian, Kayvan

doi:10.1002/ima.22076

Cited by 12 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most recently, some popular model based segmentation methods were proposed such as the statistical shape model and Atlas based segmentation [13]- [19]. The statistical shape models are used for femur segmentation.…”

Section: Figure 1 Proposed Region-based Segmentation Framework For Fmentioning

confidence: 99%

An Efficient Region Precise Thresholding and Direct Hough Transform in Femur and Femoral Neck Segmentation Using Pelvis CT

et al. 2020

View full text Add to dashboard Cite

This study proposed a fully-automated method for the segmentation of the femur and femoral neck in volumetric computed tomography (CT) images for the evaluation of osteoporotic fractures with severe abnormalities. We evaluated the proposed method on pelvis CT image of 30 patients for both the left and right sides. The proposed framework consists of three components: (1) localization of the acetabulum from the femoral head by tracing the intensity and adjacent neighbors of bone pixels, (2) segmentation and enhancement of the femur from its surrounding tissue using multi-level thresholding with filtering techniques, and (3) extraction of femoral neck contours using a directed Hough transform with oriented contour-filling techniques. The quality of the proposed femur segmentation performance was compared with the segmentation results using an edge-based active contour model (ACM), active shape model (ASM) and ground truth including average precision, recall, false-positive rate (FPR), false-negative rate (FNR), and the Dice similarity coefficient (DSC). The proposed method showed error of less than 1% for femur segmentation. A highly satisfactory similarity agreement was achieved between automated and manual methods, with a DSC greater than 94.8-exceeding those of semi-automated segmentations of the femur. Quantitative and qualitative experimental results indicated that the proposed fully-automated approach was capable of accurately segmenting the femur and femoral neck, which suggests the possibility of reducing insignificant contours of bone structures for further assessment of risk for osteoporotic fractures. INDEX TERMS Acetabulum, bone segmentation, computed tomography, femoral neck, femur.

show abstract

Section: Figure 1 Proposed Region-based Segmentation Framework For Fmentioning

confidence: 99%

An Efficient Region Precise Thresholding and Direct Hough Transform in Femur and Femoral Neck Segmentation Using Pelvis CT

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The overlapping area and the mean deviation distance are used to quantify the accuracy [ 37 ]. In this section, 245 CT images will be manually segmented by experts for each patient and the results will be the ground truth.…”

Section: Discussionmentioning

confidence: 99%

“…Five patients of the 20 patients are randomly selected, and their CT images are used to calculate the overlapping area O . The shapes presenting with an overlapping area of more than 90% are classified as “accurate”, the shapes presenting with an overlapping area of 90–80% are classified as “fair”, and the shapes presenting with an overlapping area of less than 80% are classified as “unacceptable” [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

The segmentation of bones in pelvic CT images based on extraction of key frames

Wang

Shi

et al. 2018

BMC Med Imaging

View full text Add to dashboard Cite

BackgroundBone segmentation is important in computed tomography (CT) imaging of the pelvis, which assists physicians in the early diagnosis of pelvic injury, in planning operations, and in evaluating the effects of surgical treatment. This study developed a new algorithm for the accurate, fast, and efficient segmentation of the pelvis.MethodsThe proposed method consists of two main parts: the extraction of key frames and the segmentation of pelvic CT images. Key frames were extracted based on pixel difference, mutual information and normalized correlation coefficient. In the pelvis segmentation phase, skeleton extraction from CT images and a marker-based watershed algorithm were combined to segment the pelvis. To meet the requirements of clinical application, physician’s judgment is needed. Therefore the proposed methodology is semi-automated.ResultsIn this paper, 5 sets of CT data were used to test the overlapping area, and 15 CT images were used to determine the average deviation distance. The average overlapping area of the 5 sets was greater than 94%, and the minimum average deviation distance was approximately 0.58 pixels. In addition, the key frame extraction efficiency and the running time of the proposed method were evaluated on 20 sets of CT data. For each set, approximately 13% of the images were selected as key frames, and the average processing time was approximately 2 min (the time for manual marking was not included).ConclusionsThe proposed method is able to achieve accurate, fast, and efficient segmentation of pelvic CT image sequences. Segmentation results not only provide an important reference for early diagnosis and decisions regarding surgical procedures, they also offer more accurate data for medical image registration, recognition and 3D reconstruction.

show abstract

“…Wu et al [18] proposed a new image segmentation method to extracts the pelvic on the structure. They used a combination of anatomical knowledge and computational techniques.…”

Section: Related Workmentioning

confidence: 99%

3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Maksoud

Elmogy

2016

Medical Imaging in Clinical Applications

View full text Add to dashboard Cite

3D medical images segmentation is a very difficult task. It may not be accurate and takes extreme time. In this chapter, we accurately detect the brain tumor from 3D MRI image with less time. The 3D image consists of multiple 2D slices. Segmenting each 2D slice by using the 2D techniques gives more accuracy rather than segmenting the whole 3D image. The integration between K-Means and Particle Swarm Optimization was proposed to segment the 2D MRI slices of the 3D MRI image. We solved the time problem of segmenting all 2D slices of the 3D image. The experiments emphasized the effectiveness of our proposed system in segmenting the 2D and 3D medical images. It achieved 100 % accuracy for the tested 3D dataset and 98.75 % average accuracy for all tested 2D and 3D datasets. The proposed integration reduced time by a mean of 10 min for the tested 2D and 3D datasets.

show abstract

Bone segmentation and 3D visualization of CT images for traumatic pelvic injuries

Cited by 12 publications

References 25 publications

An Efficient Region Precise Thresholding and Direct Hough Transform in Femur and Femoral Neck Segmentation Using Pelvis CT

An Efficient Region Precise Thresholding and Direct Hough Transform in Femur and Femoral Neck Segmentation Using Pelvis CT

The segmentation of bones in pelvic CT images based on extraction of key frames

3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Contact Info

Product

Resources

About