scholarly journals Histogram-Based Texture Characterization and Classification of Brain Tissues in Non-Contrast CT Images of Stroke Patients

10.5772/65349 ◽  
2016 ◽  
Author(s):  
Kenneth K. Agwu ◽  
Christopher C. Ohagwu
Keyword(s):  
Ct Images ◽  
Stroke Patients ◽  
Contrast Ct ◽  
Texture Characterization ◽  
Brain Tissues

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

scholarly journals Classification of Aortic Dissection and Rupture on Post-contrast CT Images Using a Convolutional Neural Network

2019 ◽  
Vol 32 (6) ◽  
pp. 939-946 ◽  
Author(s):  
Robert J. Harris ◽  
Shwan Kim ◽  
Jerry Lohr ◽  
Steve Towey ◽  
Zeljko Velichkovich ◽  
...  
Keyword(s):  
Neural Network ◽  
Aortic Dissection ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Post Contrast ◽  
Contrast Ct

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

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 Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
Fdg Pet Ct

Aim. To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods. We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results. The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

Sign in / Sign up

Export Citation Format

Share Document