scholarly journals A Semi-automated Segmentation of Soil X-ray Microtomography

2015 ◽  
Vol 46 ◽  
pp. 1468-1475 ◽  
Author(s):  
Ajay K. Mandava ◽  
Emma E. Regentova ◽  
Markus Berli
Keyword(s):  
Automated Segmentation ◽  
X Ray

Automated segmentation of murine lung tumors in x-ray micro-CT images

2014 ◽  
Author(s):  
Joshua K. Y. Swee ◽  
Clare Sheridan ◽  
Elza de Bruin ◽  
Julian Downward ◽  
Francois Lassailly ◽  
...  
Keyword(s):  
Lung Tumors ◽  
Ct Images ◽  
Automated Segmentation ◽  
Micro Ct ◽  
Murine Lung ◽  
X Ray

scholarly journals Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray

2021 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Sándor Kónya ◽  
TR Sai Natarajan ◽  
Hassan Allouch ◽  
KaisAbu Nahleh ◽  
OmneyaYakout Dogheim ◽  
...  
Keyword(s):  
Neural Network ◽  
Lumbar Spine ◽  
Convolutional Neural Network ◽  
Automated Segmentation ◽  
X Ray

scholarly journals Automated segmentation of hepatic vessel trees in non-contrast x-ray CT images

2007 ◽  
Author(s):  
Suguru Kawajiri ◽  
Xiangrong Zhou ◽  
Xuejin Zhang ◽  
Takeshi Hara ◽  
Hiroshi Fujita ◽  
...  
Keyword(s):  
Ct Images ◽  
Automated Segmentation ◽  
X Ray

scholarly journals A New Straightforward Method for Automated Segmentation of Trabecular Bone from Cortical Bone in Diverse and Challenging Morphologies

2021 ◽  
Author(s):  
Eva C. Herbst ◽  
Alessandro A. Felder ◽  
Lucinda A. E. Evans ◽  
Sara Ajami ◽  
Behzad Javaheri ◽  
...  
Keyword(s):  
Cortical Bone ◽  
Skeletal Structure ◽  
Automated Segmentation ◽  
Micro Computed Tomography ◽  
X Ray ◽  
Straightforward Method ◽  
Successful Separation ◽  
And Function ◽  
Automated Protocol ◽  
User Friendly

AbstractMany physiological, biomechanical, evolutionary and clinical studies that explore skeletal structure and function require successful separation of trabecular from cortical compartments of a bone that has been imaged by X-ray micro-computed tomography (microCT) prior to analysis. Separation is often time-consuming, involves user bias and needs manual sub-division of these two similarly radio-opaque compartments. We have developed an objective, automated protocol which reduces user bias and enables straightforward, user-friendly segmentation of trabecular from cortical bone without requiring sophisticated programming expertise. This method can conveniently be used as a “recipe” in commercial programmes (Avizo herein) and applied to a variety of datasets. Here, we characterise and share this recipe, and demonstrate its application to a range of murine and human bone types, including normal and osteoarthritic specimens, and bones with distinct embryonic origins and spanning a range of ages. We validate the method by testing inter-user bias during the scan preparation steps and confirm utility in the architecturally challenging analysis of growing murine epiphyses. We also report details of the recipe, so that other groups can readily re-create a similar method in open access programs. Our aim is that this method will be adopted widely to create a more standardized and time efficient method of segmenting trabecular and cortical bone.

Computer-based automated analysis of X-ray and thermal imaging of knee region in evaluation of rheumatoid arthritis

2017 ◽  
Vol 231 (12) ◽  
pp. 1178-1187 ◽  
Author(s):  
U Snekhalatha ◽  
T Rajalakshmi ◽  
M Gopikrishnan ◽  
Nilkantha Gupta
Keyword(s):  
Rheumatoid Arthritis ◽  
Thermal Imaging ◽  
Automated Segmentation ◽  
Statistical Features ◽  
Segmentation Method ◽  
X Ray ◽  
Knee Region ◽  
Erythrocyte Sedimentation ◽  
Occurrence Matrix ◽  
Snake Algorithm

The aim and objectives of the study are as follows: (1) to perform automated segmentation of knee X-ray images using fast greedy snake algorithm and feature extraction using gray level co-occurrence matrix method, (2) to implement automated segmentation of knee thermal image using RGB segmentation method and (3) to compare the features extracted from the segmented knee region of X-ray and thermal images in rheumatoid arthritis patients using a biochemical method as standard. In all, 30 rheumatoid arthritis patients and 30 age- and sex-matched healthy volunteers were included in the study. X-ray and thermography images of knee regions were acquired, and biochemical tests were carried out subsequently. The X-ray images were segmented using fast greedy snake algorithm, and feature extractions were performed using gray level co-occurrence matrix method. The thermal image was segmented using RGB-based segmentation method and statistical features were extracted. Statistical features extracted after segmentation from X-ray and thermal imaging of knee region were correlated with the standard biochemical parameters. The erythrocyte sedimentation rate shows statistically significant correlations (p < 0.01) with the X-ray parameters such as joint space width and % combined cortical thickness. The skin surface temperature measured from knee region of thermal imaging was highly correlated with erythrocyte sedimentation rate. Among all the extracted features namely mean, variance, energy, homogeneity and difference entropy depict statistically significant percentage differences between the rheumatoid arthritis and healthy subjects. From this study, it was observed that thermal infrared imaging technique serves as a potential tool in the evaluation of rheumatoid arthritis at an earlier stage compared to radiography. Hence, it was predicted that thermal imaging method has a competency in the diagnosis of rheumatoid arthritis by automated segmentation methods.

Semi-automated segmentation and visualization of complex undisturbed root systems with X-ray μCT

2019 ◽  
Vol 192 ◽  
pp. 59-65 ◽  
Author(s):  
Peter Maenhout ◽  
Steven Sleutel ◽  
Hui Xu ◽  
Luc Van Hoorebeke ◽  
Veerle Cnudde ◽  
...  
Keyword(s):  
Root Systems ◽  
Automated Segmentation ◽  
X Ray

Automated segmentation of middle hepatic vein in non-contrast x-ray CT images based on an atlas-driven approach

2008 ◽  
Author(s):  
Teruhiko Kitagawa ◽  
Xiangrong Zhou ◽  
Takeshi Hara ◽  
Hiroshi Fujita ◽  
Ryujiro Yokoyama ◽  
...  
Keyword(s):  
Hepatic Vein ◽  
Middle Hepatic Vein ◽  
Ct Images ◽  
Automated Segmentation ◽  
X Ray

scholarly journals Automated segmentation of knee thermal imaging and X-ray in evaluation of rheumatoid arthritis

2018 ◽  
Vol 7 (2.8) ◽  
pp. 326 ◽  
Author(s):  
U Snekhalatha ◽  
T Rajalakshmi ◽  
M Gobikrishnan
Keyword(s):  
Rheumatoid Arthritis ◽  
Thermal Imaging ◽  
Detection Algorithm ◽  
Thermal Image ◽  
Automated Segmentation ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
X Ray ◽  
Canny Edge ◽  
Canny Edge Detection Algorithm

Rheumatoid arthritis (RA) is a long lasting autoimmune disorder that affects the multiple joints of human body. The aim and objective of the study was i) to implement the automated segmentation of knee x-ray image and thermal image using fuzzy c means  and canny edge detection algorithm. ii) To compare both the imaging modalities by means of feature extraction and correlate with the biochemical method as standard. Fifteen subjects with RA in knee region and 15 healthy controls were included in this study. The segmentation of thermal images was performed using fuzzy c-means algorithm and x-ray segmentation was implemented using canny edge detection algorithm. The skin surface temperature weremeasured in the thermal image of knee regionin both RA and control subjects. The features wereextracted from the segmented region of the knee x-ray image. The automated segmentation implemented in thermal imaging provided better results compared to x-ray image segmentation process. The thermal imaging feature and x-ray imaging features correlated significantly with the standard parameters.

Sign in / Sign up

Export Citation Format

Share Document