scholarly journals INTRODUCING SHAPE CONSTRAINT VIA LEGENDRE MOMENTS IN A VARIATIONAL FRAMEWORK FOR CARDIAC SEGMENTATION ON NON-CONTRAST CT IMAGES

2010 ◽  
Keyword(s):  
Ct Images ◽  
Shape Constraint ◽  
Cardiac Segmentation ◽  
Variational Framework ◽  
Legendre Moments ◽  
Contrast Ct

scholarly journals Morphometrical Study of the Lumbar Segment of the Internal Vertebral Venous Plexus in Dogs: A Contrast CT-Based Study

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1502
Author(s):  
Valeria Ariete ◽  
Natalia Barnert ◽  
Marcelo Gómez ◽  
Marcelo Mieres ◽  
Bárbara Pérez ◽  
...  
Keyword(s):  
Epidural Space ◽  
Ct Images ◽  
Venous Plexus ◽  
Morphological Pattern ◽  
Vertebral Canal ◽  
Dural Sac ◽  
Internal Vertebral Venous Plexus ◽  
Adjacent Structures ◽  
Contrast Ct ◽  
Enhanced Computed Tomography

The internal vertebral venous plexus (IVVP) is a thin-walled, valveless venous network that is located inside the vertebral canal, communicating with the cerebral venous sinuses. The objective of this study was to perform a morphometric analysis of the IVVP, dural sac, epidural space and vertebral canal between the L1 and L7 vertebrae with contrast-enhanced computed tomography (CT). Six clinically healthy adult dogs weighing between 12 kg to 28 kg were used in the study. The CT venographic protocol consisted of a manual injection of 880 mgI/kg of contrast agent (587 mgI/kg in a bolus and 293 mgI/mL by continuous infusion). In all CT images, the dimensions of the IVVP, dural sac, and vertebral canal were collected. Dorsal reconstruction CT images showed a continuous rhomboidal morphological pattern for the IVVP. The dural sac was observed as a rounded isodense structure throughout the vertebral canal. The average area of the IVVP ranged from 0.61 to 0.74 mm2 between L1 and L7 vertebrae (6.3–8.9% of the vertebral canal), and the area of the dural sac was between 1.22 and 7.42 mm2 (13.8–72.2% of the vertebral canal). The area of the epidural space between L1 and L7 ranged from 2.85 to 7.78 mm2 (27.8–86.2% of the vertebral canal). This CT venography protocol is a safe method that allows adequate visualization and morphometric evaluation of the IVVP and adjacent structures.

scholarly journals Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

2018 ◽  
Vol 7 (2.6) ◽  
pp. 306
Author(s):  
Aravinda H.L ◽  
M.V Sudhamani
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Ct Images ◽  
Higher Order ◽  
Liver Carcinoma ◽  
Tumor Segmentation ◽  
Abdominal Ct ◽  
Simple Linear Iterative Clustering ◽  
Classification Of Tumors ◽  
Legendre Moments

The major reasons for liver carcinoma are cirrhosis and hepatitis.  In order to  identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

Quantitative analysis of brain herniation from non-contrast CT images using deep learning

2021 ◽  
Vol 349 ◽  
pp. 109033
Author(s):  
Manas Kumar Nag ◽  
Akshat Gupta ◽  
A.S. Hariharasudhan ◽  
Anup Kumar Sadhu ◽  
Abir Das ◽  
...  
Keyword(s):  
Deep Learning ◽  
Quantitative Analysis ◽  
Ct Images ◽  
Brain Herniation ◽  
Contrast Ct

Comparison of phase contrast CT images for different tumor tissues

2013 ◽  
Author(s):  
Chenglin Liu ◽  
Hua Xu ◽  
Xiaohua Wang ◽  
Dongming Zhang ◽  
Xinyi Zhang ◽  
...  
Keyword(s):  
Phase Contrast ◽  
Ct Images ◽  
Tumor Tissues ◽  
Contrast Ct

scholarly journals Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images

2017 ◽  
Author(s):  
P. Nardelli ◽  
D. Jimenez-Carretero ◽  
D. Bermejo-Pelaez ◽  
M.J. Ledesma-Carbayo ◽  
Farbod N. Rahaghi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Artery ◽  
Learning Strategy ◽  
Ct Images ◽  
Contrast Ct
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