A New Framework for the Accurate FE Model of Bone Tissue Based on CT Images

2010 ◽  
Vol 44-47 ◽  
pp. 1612-1616
Author(s):  
Xiao Hui Huang ◽  
Guo Qun Zhao ◽  
Wen Guang Liu ◽  
Pei Lai Liu
Keyword(s):  
Bone Tissue ◽  
Material Property ◽  
Geometric Model ◽  
Automatic Segmentation ◽  
Region Of Interest ◽  
Ct Images ◽  
Fe Model ◽  
Volume Of Interest ◽  
Smooth Operator ◽  
New Framework

The frameworks for finite element (FE) model of bone tissue available in pervious literatures, to some extent, are expert-oriented and give rise to a considerable deviation in geometric model and assignment of material property. The objective of this study is to develop a new framework to reconstruct accurate individual bone FE model based on CT images rapidly and conveniently. In image-processing, automatic segmentation of the region of interest (ROIs) improves the efficiency. The idea of enclosed volume of interest (VOI) overcomes the drawback of geometric ambiguity in Marching Cube (MC) method. Geometric model is easily obtained by a STL translator and smooth operator in home-made program. In the material property assignment, two templates for hexahedron and tetrahedron FE models, respectively, are put forth to smoothing an abrupt change of material property in the region from cortical to cancellous. K-mean algorithm is introduced to cluster material properties to improve partition performance. Finally, the new framework is demonstrated by the implementation of a femoral FE model.

scholarly journals A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jared Hamwood ◽  
Beat Schmutz ◽  
Michael J. Collins ◽  
Mark C. Allenby ◽  
David Alonso-Caneiro
Keyword(s):  
Deep Learning ◽  
Human Performance ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Human Observer ◽  
In Series ◽  
Magnetic Resonance Imaging Mri ◽  
High Degree ◽  
Orbital Region ◽  
Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet

2020 ◽  
Author(s):  
Qi Yang ◽  
Yunke Li ◽  
Mengyi Zhang ◽  
Tian Wang ◽  
Fei Yan ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ct Images

GROUND-GLASS OPACITY DETECTION BY USING CORRELATION BETWEEN SUCCESSIVE SLICE IMAGES

2007 ◽  
Vol 16 (04) ◽  
pp. 583-592 ◽  
Author(s):  
HYOUNGSEOP KIM ◽  
MASAKI MAEKADO ◽  
JOO KOOI TAN ◽  
SEIJI ISHIKAWA ◽  
MASAAKI TSUKUDA
Keyword(s):  
Soft Tissues ◽  
Automatic Segmentation ◽  
Ground Glass Opacity ◽  
Processing Technique ◽  
Ct Images ◽  
Image Processing Technique ◽  
Ground Glass ◽  
Lung Region ◽  
Mdct Image ◽  
Thorax Ct

Medical imaging systems such as computed tomography, magnetic resonance imaging provided a high resolution image for powerful diagnostic tool in visual inspection fields by physician. Especially MDCT image can be used to obtain detailed images of the pulmonary anatomy, including pulmonary diseases such as the pulmonary nodules, the pulmonary vein, etc. In the medical image processing technique, segmentation is a difficult task because surrounding soft tissues and organs have similar CT values and sometimes contact with each other. We propose a new technique for automatic segmentation of lung regions and its classification for ground-glass opacity from the extracted lung regions by computer based on a set of the thorax CT images. In this paper, we segment the lung region for extraction of the region of interest employing binarization and labeling process from the inputted each slices images. The region having the largest area is regarded as the tentative lung regions. Furthermore, the ground-glass opacity is classified by correlation distribution on the slice to slice from the extracted lung region with respect to the thorax CT images. Experiment is performed employing twenty six thorax CT image sets and 96% of recognition rates were achieved. Obtained results are shown along with a discussion.

Bone Ingrowth Around an Uncemented Femoral Implant Using Mechanoregulatory Algorithm: A Multiscale Finite Element Analysis

2021 ◽  
Author(s):  
Basil Mathai ◽  
Sanjay Gupta
Keyword(s):  
Finite Element ◽  
Bone Tissue ◽  
Bone Ingrowth ◽  
Progressive Increase ◽  
Stair Climbing ◽  
Porous Coating ◽  
Fe Model ◽  
Implant Surface ◽  
Element Analysis ◽  
Medial Side

Abstract The primary fixation and long-term stability of a cementless femoral implant depend on bone ingrowth within the porous coating. Although attempts were made to quantify the peri-implant bone ingrowth using the finite element (FE) analysis and mechanoregulatory principles, the tissue differentiation patterns on a porous-coated hip stem have scarcely been investigated. The objective of this study is to predict the spatial distribution of evolutionary bone ingrowth around an uncemented hip stem, using a 3D multiscale mechanobiology based numerical framework. Multiple load cases representing a variety of daily living activities, including walking, stair climbing, sitting down and standing up from a chair, were used as applied loading conditions. The study accounted for the local variations in host bone material properties and implant-bone relative displacements of the macroscale implanted FE model, in order to predict bone ingrowth in microscale representative volume elements (RVEs) of twelve interfacial regions. In majority RVEs, 20-70% bone tissue (immature and mature) was predicted after two months, contributing towards a progressive increase in average Young's modulus (1200-3000 MPa) of the inter-bead tissue layer. Higher bone ingrowth (mostly greater than 60%) was predicted in the antero-lateral regions of the implant, as compared to the postero-medial side (20-50%). New bone tissue was formed deeper inside the inter-bead spacing, adhering to the implant surface. The study helps to gain an insight into the degree of osseointegration of a porous-coated femoral implant.

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks

2018 ◽  
Vol 41 (4) ◽  
pp. 1009-1020 ◽  
Author(s):  
Mina Zareie ◽  
Hossein Parsaei ◽  
Saba Amiri ◽  
Malik Shahzad Awan ◽  
Mohsen Ghofrani
Keyword(s):  
Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
3D Ct ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks

A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

2014 ◽  
Vol 721 ◽  
pp. 783-787
Author(s):  
Shao Hu Peng ◽  
Hyun Do Nam ◽  
Yan Fen Gan ◽  
Xiao Hu
Keyword(s):  
Multiscale Analysis ◽  
Automatic Segmentation ◽  
Local Variation ◽  
Recognition System ◽  
Ct Images ◽  
Gray Level ◽  
Ct Image ◽  
Image Pyramid ◽  
Level Variation ◽  
X Ray

Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this paper presents a robust line filter based on the local gray level variation and multiscale analysis. The proposed line filter makes usage of the local gray level and its local variation to enhance line-like regions in the X-ray/CT image, which can well overcome the problems of the image noises and non-uniform intensity of the images. For detecting various sizes of line-like regions, an image pyramid is constructed based on different neighboring distances, which enables the proposed filter to analyze different sizes of regions independently. Experimental results showed that the proposed line filter can well segment various sizes of line-like regions in the X-ray/CT images, which are with image noises and non-uniform intensity problems.

Sign in / Sign up

Export Citation Format

Share Document