scholarly journals Automatic Recognition of Anatomical Regions in Computed Tomography Images

2018 ◽  
Vol 62 (4) ◽  
pp. 117-125 ◽  
Author(s):  
Márton József Tóth ◽  
László Ruskó ◽  
Balázs Csébfalvi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Human Body ◽  
Spatial Coherence ◽  
Automatic Recognition ◽  
Processing Method ◽  
Post Processing ◽  
Computed Tomography Images ◽  
The Neural Network ◽  
Initial Classification

This paper presents a method that can recognize anatomy regions in Computed Tomography (CT) examinations. In this work the human body is divided into eleven regions from the foot to the head. The proposed method consists of two main parts. In the first step, a Convolutional Neural Network (CNN) is used to classify the axial slices of the CT exam. The accuracy of the initial classification is 93.4 %. As the neural network processes the axial slices independently from each other, no spatial coherence is guaranteed. To ensure the contentious labeling the initial classification step is followed by a post-processing method that incorporates the expected order and size of the anatomical regions to improve the labeling. In this way, the accuracy is increased to 94.0 %, the confusion of non-neighboring regions dropped from 1.5 % to 0.0 %. This means that a continuous and outlier free labeling is obtained. The method was trained on a set of 320 CT exams and evaluated on another set of 160 cases.

An Optimization of System for Automatic Recognition of Ischemic Stroke Areas in Computed Tomography Images

Informatica ◽  
2007 ◽  
Vol 18 (4) ◽  
pp. 603-614
Author(s):  
Darius Grigaitis ◽  
Vaida Bartkutė ◽  
Leonidas Sakalauskas
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Automatic Recognition ◽  
Computed Tomography Images

scholarly journals Metal artifact reduction in computed tomography images based on developed generative adversarial neural network

2021 ◽  
Vol 24 ◽  
pp. 100573
Author(s):  
Goli Khaleghi ◽  
Mohammad Hosntalab ◽  
Mahdi Sadeghi ◽  
Reza Reiazi ◽  
Seied Rabi Mahdavi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images

Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features

2011 ◽  
Vol 328-330 ◽  
pp. 1763-1767
Author(s):  
Jian Qiang Shen ◽  
Xuan Zou
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Radon Transform ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Line Detection ◽  
Original Image ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for measuring fabric texture orientations and recognizing weave patterns. Wavelet transform is suited for fabric image decomposition and Radon Transform is fit for line detection in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet transform, these orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify double layer and some derivative twill weaves such as angular twill and pointed twill.

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

2020 ◽  
Vol 134 (4) ◽  
pp. 328-331 ◽  
Author(s):  
P Parmar ◽  
A-R Habib ◽  
D Mendis ◽  
A Daniel ◽  
M Duvnjak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Network ◽  
Middle Turbinate ◽  
Concha Bullosa ◽  
Computed Tomography Images ◽  
Computed Tomography Scans ◽  
Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

scholarly journals Kidney segmentation from computed tomography images using deep neural network

2020 ◽  
Vol 123 ◽  
pp. 103906 ◽  
Author(s):  
Luana Batista da Cruz ◽  
José Denes Lima Araújo ◽  
Jonnison Lima Ferreira ◽  
João Otávio Bandeira Diniz ◽  
Aristófanes Corrêa Silva ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Neural Network ◽  
Computed Tomography Images ◽  
Kidney Segmentation

scholarly journals Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 202459-202468
Author(s):  
Xiao Lou ◽  
Youzhe Zhu ◽  
Kumaradevan Punithakumar ◽  
Lawrence H. Le ◽  
Baosheng Li
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Computed Tomography Images ◽  
Labeling Method

Gestures Identification for Myoelectric Based Control of the Robotic Arm

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Adhau P ◽  
◽  
Kadwane S. G ◽  
Shital Telrandhe ◽  
Rajguru V. S ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Human Hand ◽  
Robot Interaction ◽  
The Neural Network

Human robot interaction have been ever the topic of research to research scholars owing to its importance to help humanity. Robust human interacting robot where commands from Electromyogram (EMG) signals is recently being investigated. This article involves study of motions a system that allows signals recorded directly from a human body and thereafter can be used for control of a small robotic arm. The various gestures are recognized by placing the electrodes or sensors on the human hand. These gestures are then identified by using neural network. The neural network will thus train the signals. The offline control of the arm is done by controlling the motors of the robotic arm.

Fabric Weave Pattern’s Recognition Based on Texture Orientation Features

2013 ◽  
Vol 423-426 ◽  
pp. 2404-2408 ◽  
Author(s):  
Jian Qiang Shen ◽  
Ge Ge Li ◽  
Xuan Zou ◽  
Yan Li
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Image Decomposition ◽  
Automatic Recognition ◽  
Wavelet Packet Transform ◽  
Original Image ◽  
Energy Distributions ◽  
Novel Approach ◽  
The Neural Network ◽  
Texture Orientation

A novel approach is proposed for representing fabric texture orientations and recognizing weave patterns. Wavelet packet transform is suited for fabric image decomposition in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet packet transform, and the regular orientations can be represented as the energy distributions of these images because the average energies of different fabric texture directions are changeable in a certain way. These energy orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify some complex weaves.

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