Fast segmentation of bone in CT images using 3D adaptive thresholding

Zhang, J.; Yan, Chye Hwang; Chui, Chee-Kong; Ong, Sim Heng

doi:10.1016/j.compbiomed.2009.11.020

Cited by 114 publications

(68 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recent research on influence of different tissues [34] on ECG signals suggests that muscle, lungs and fat tissues are more important than the others. Most of these tissues can be segmented automatically [35][36][37][38]. In this paper, we will focus on segmentation of abdominal parenchymal organs.…”

Section: Segmentation Techniquesmentioning

confidence: 99%

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

2016

View full text Add to dashboard Cite

Abstract:In this study, we present several image segmentation techniques for various image scales and modalities. We consider cellular-, organ-, and whole organism-levels of biological structures in cardiovascular applications. Several automatic segmentation techniques are presented and discussed in this work. The overall pipeline for reconstruction of biological structures consists of the following steps: image pre-processing, feature detection, initial mask generation, mask processing, and segmentation post-processing. Several examples of image segmentation are presented, including patient-specific abdominal tissues segmentation, vascular network identification and myocyte lipid droplet micro-structure reconstruction.

show abstract

Section: Segmentation Techniquesmentioning

confidence: 99%

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

2016

View full text Add to dashboard Cite

show abstract

“…Due to the absence of the source code for the state of the art methods such as [6] and [7] a fair comparison would be difficult specially with variety of parameter choices available. The Dice coefficient of each sample of 9 volumetric images has been calculated.…”

Section: Feature Extraction Using the Pressure Analogymentioning

confidence: 99%

“…Other methods relied on initial parameters chosen by an operator to refine boundary information between the acetabulum and the femoral bone [5]. There are two automated approaches to bone segmentation in the literature, one is based on the graphcut method using statistical analysis and morphology [6] and the other one hinged on local adaptive thresholding conditioned on Bayesian rules [7].…”

Section: Introductionmentioning

confidence: 99%

Femur Bone Segmentation Using a Pressure Analogy

Alathari

Nixon

Bah

2014

2014 22nd International Conference on Pattern Recognition

View full text Add to dashboard Cite

Abstract-It has been recently shown that preclinical analysis of computed tomography 3D image volumes can provide essential information to find the optimal position of an implant in hip replacement procedures. In order to extract such data, proper segmentation is crucial. Many of the currently-available methods depend on manually segmented data as the first step. Inherent difficulties concern the similar density of adjacent structures, and that physically-separated structures appear to touch in scanned imagery. In this study, we describe a new technique based on pressure analogy that depends on the local features of the image to accurately and automatically segment and visualize the femur bone and separate it from the acetabulum. The Dice coefficient was employed to study the similarity between the surface area of the segmentations compared with the manually segmented data, and a high value has been achieved. The same method also showed promising results in segmenting other limbs such as the pelvis, tibia and fibula bones.

show abstract

“…Many other segmentation methods exist, such as the local adaptive threshold, but their description is beyond the scope of this thesis and are seldomly used in the clinical field [91,93]. The most common technique used in this field to isolate a structure from the rest of the data is called binary thresholding, which is applied in the following manner.…”

Section: Segmentation By Thresholdingmentioning

confidence: 99%

Structural properties of the mastoid using image analysis and visualization

Cros¹

2017

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

Volume rendering of a 3D shape analysis where medial balls are fitting the mastoid air cell system of a human temporal bone specimen, overlaid over a manually cropped version of the original data. The different colours represent the different size classes: from very small in dark blue to vary large in purple. The bone is rendered with a modified transfer function so as to reveal the micro-channels.Copyright © 2017 Olivier Cros, unless otherwise noted. AbstractThe mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented in a clinical CT scan. Where the conventional clinical CT scanner lacks level of minute details, micro-CT scanning provides an overwhelming amount of fine details. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings.This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously applied for conventional clinical CT scans. The study compared current results with previous studies, with successive downsampling the data down to a resolution found in conventional clinical CT scans. The results from the statistical analysis showed that all the small mastoid air cells, that cannot be detected in conventional clinical CT scans, do heavily contribute to the estimation of the surface area, and in consequence to the estimation of the surface area to volume ratio by a factor of about 2.6. Such a result further strengthens the idea of the mastoid to play an active role in pressure regulation and gas exchange.Discovery of micro-channels through specific use of a non-traditional transfer function was then reported, where a qualitative and a quantitative preanalysis were performed and reported. To gain more knowledge about these micro-channels, a local structure tensor analysis was applied where structures are described in terms of planar, tubular, or isotropic structures. The results from this structural tensor analysis suggest these micro-channels to potentially be part of a more complex framework, which hypothetically would provide a separate blood supply for the mucosa lining the mastoid air cell system.The knowledge gained from analysing the micro-channels as locally providing blood to the mucosa, led to the consideration of how inflammation of the mucosa could impact the pneumatization of the mastoid air cell system. Though very primitive, a 3D shape analysis of the mastoid air cell system was carried out. The mastoid air cell system was first represented in a compact form through a medial axis, from which medial balls could be used. The medial balls, representative of how large the mastoid air cells can be locally, were used in two co...

show abstract

Fast segmentation of bone in CT images using 3D adaptive thresholding

Cited by 114 publications

References 25 publications

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

Image Segmentation for Cardiovascular Biomedical Applications at Different Scales

Femur Bone Segmentation Using a Pressure Analogy

Structural properties of the mastoid using image analysis and visualization

Contact Info

Product

Resources

About