An Automated Cardiac Ventricle Segmentation on CMR Images Using Grey-Level Mask R-CNN

2021 ◽  
Author(s):  
Hsiao-Chi Li ◽  
Kuan-Yu Chen ◽  
Shih-Hsien Sung ◽  
Chun-Ku Chen
Keyword(s):  
Grey Level ◽  
Cardiac Ventricle ◽  
Ventricle Segmentation

scholarly journals Automatic cardiac ventricle segmentation in MR images: a validation study

2010 ◽  
Vol 6 (5) ◽  
pp. 573-581 ◽  
Author(s):  
Damien Grosgeorge ◽  
Caroline Petitjean ◽  
Jérôme Caudron ◽  
Jeannette Fares ◽  
Jean-Nicolas Dacher
Keyword(s):  
Validation Study ◽  
Mr Images ◽  
Cardiac Ventricle ◽  
Ventricle Segmentation

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Three-dimensional active shape model matching for left ventricle segmentation in cardiac CT

2003 ◽  
Author(s):  
Hans C. van Assen ◽  
Rob J. van der Geest ◽  
Mikhail G. Danilouchkine ◽  
Hildo J. Lamb ◽  
Johan H. C. Reiber ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Cardiac Ct ◽  
Three Dimensional ◽  
Active Shape Model ◽  
Model Matching ◽  
Shape Model ◽  
Active Shape ◽  
Ventricle Segmentation

Visual Recognition System of Elastic Rail Clips for Mass Rapid Transit Systems

2007 ◽  
Author(s):  
Hsiang-Yu Hsieh ◽  
Nanming Chen ◽  
Ching-Lung Liao
Keyword(s):  
Visual Recognition ◽  
Experimental Testing ◽  
Recognition System ◽  
Morphological Processing ◽  
Driving Safety ◽  
Rapid Transit ◽  
Grey Level ◽  
Railway Transportation ◽  
Transit Systems ◽  
Mass Rapid Transit

In recent years, the railway transportation system has become one of the main means of transportation. Therefore, driving safety is of great importance. However, because of the potential of multiple breaks of elastic rail clips in a fixed rail, accidents may occur when a train passes through the track. This paper presents the development of a computer visual recognition system which can detect the status of elastic rail clips. This visual recognition system can be used in mass rapid transit systems to reduce the substantial need of manpower for checking elastic rail clips at present. The visual recognition system under current development includes five components: preprocessing, identification of rail position, search of elastic rail clip regions, selection of the elastic rail clip, and recognition of the elastic rail clip. The preprocessing system transforms the colored images into grey-level images and eliminates noises. The identification of rail position system uses characteristics of the grey-level variation and confirms the rail position. The search system uses wavelet transformation to carry out the search of elastic rail clip regions. The selection system finds a suitable threshold, using techniques from morphological processing, object search and image processing. The recognition system processes characteristics and structures of elastic rail clips. Experimental testing shows the ability of the developed system to recognize both normal elastic rail clip images and broken elastic rail clip images. This result confirms the feasibility in developing such a visual recognition system.

Near-Band IR Small Scene Star Image Segmentation Based on Partial Histogram Grey Level

2014 ◽  
Vol 496-500 ◽  
pp. 1834-1839
Author(s):  
Zhe Wang ◽  
Zhe Yan ◽  
Wei Tan
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Real Time ◽  
Grey Level ◽  
Star Image ◽  
Star Images

The near-band IR star images segmentation and recognition is key technique in day time star navigation. Due to the scene of near-band IR star imaging relative small and stellar with high star grade are limited. Pertinence and dynamic grey level threshold is necessary for image processing arithmetic. In order to enhance near-band IR star images segmentation and recognition in real-time, this paper present the process of partial histogram grey level threshold and improve for actually near-band IR star images with scene of no more than 1.5°×1.5°. It can reduce the calculation of near-band IR star images with adjustable threshold. And get rid of disturbance of small imaging square stars and noise points.

An intelligent COVID-19 classification model using optimal grey-level co-occurrence matrix features with extreme learning machine

2021 ◽  
Vol 65 (4) ◽  
pp. 334
Author(s):  
Pavan Kumar Paruchuri ◽  
V. Gomathy ◽  
E. Anna Devi ◽  
Shweta Sankhwar ◽  
S.K. Lakshmanaprabu
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Grey Level ◽  
Learning Machine ◽  
Occurrence Matrix
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