Certain Investigation On Fully Automated Method To Segment And Measure The Volume Of Pleura Effusion On Ct Images

2013 ◽  
Vol 3 (4) ◽  
pp. 13-18
Author(s):  
Punitha M. ◽  
◽  
Vijaykumar J. ◽  
Keyword(s):  
Ct Images ◽  
Automated Method ◽  
Pleura Effusion

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Seyed Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
High Speed ◽  
Early Stage ◽  
Proper Time ◽  
Ct Images ◽  
Automated System ◽  
Image Processing Algorithm ◽  
Convolutional Network ◽  
Automated Method ◽  
Feature Pyramid

COVID-19 is a severe global problem, and one of the primary ways to decrease its casualties is the infected person's identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage. In this paper, we aim to propose a high- speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed automated system takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this system runs the proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be processed, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a new modified deep convolutional network that is based on ResNet50V2 and is enhanced by the feature pyramid network for classifying the selected CT images into COVID-19 or normal. After running these two phases, if enough number of chosen CT scan images of a patient be identified as COVID-19, the system considers that patient, infected to this disease. In the single image classification stage, the ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images. At the fully automated phase, the automated system correctly identified almost 237 patients from 245 patients on average between five-folds with high speed. In the end, we also investigate the classified images with a feature visualization algorithm to indicate the area of infections in each image. We are implementing these materials on some medical centers in Iran, and we hope that it would be a great help in Intelligence disease detection anywhere.

scholarly journals Security Challenges and Application for Underwater Wireless Sensor Network

10.29007/ctsn ◽  
2018 ◽  
Author(s):  
Sarvesh Kumar Kumar ◽  
Bersha Kumari ◽  
Harshita Chawla
Keyword(s):  
Ct Images ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Normal Brain ◽  
Brain Images ◽  
Brain Ct ◽  
Automated Method ◽  
Occurrence Matrix

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
Early Stage ◽  
Proper Time ◽  
Ct Images ◽  
Image Sequences ◽  
Image Processing Algorithm ◽  
Automated Method ◽  
The World ◽  
Feature Pyramid ◽  
Lung Ct

COVID-19 is a severe global problem that has crippled many industries and killed many people around the world. One of the primary ways to decrease the casualties is the infected person's identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage so that it can help many organizations. In this paper, we aim to propose a fully-automated method to detect COVID-19 from the patient's CT scan without needing a clinical technician. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed network takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this network runs an image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be identified as normal or COVID-19, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a modified version of ResNet50V2 that is enhanced by a feature pyramid network for classifying the selected CT images into COVID-19 or normal. If enough number of chosen CT scan images of a patient be identified as COVID-19, the network considers that patient, infected to this disease. The ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images and correctly identified almost 237 patients from 245 patients.

scholarly journals Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images

2021 ◽  
Vol 16 (2) ◽  
pp. 87-104
Author(s):  
Vijayalaxmi Mekali ◽  
Girijamma H. A.
Keyword(s):  
Lung Parenchyma ◽  
Ct Images ◽  
Experimental Result ◽  
Computational Time ◽  
Detection Rates ◽  
Iterative Thresholding ◽  
Automated Method ◽  
Segmentation Approach ◽  
Thresholding Algorithm ◽  
Lung Lobes

Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.

scholarly journals Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2062
Author(s):  
Benyameen Keelson ◽  
Luca Buzzatti ◽  
Jakub Ceranka ◽  
Adrián Gutiérrez ◽  
Simone Battista ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Computer Tomography ◽  
Reference Frames ◽  
Intraclass Correlation ◽  
Ct Images ◽  
Joint Kinematics ◽  
Anatomical Landmarks ◽  
Automated Method ◽  
Landmark Identification

Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Seyed Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
High Speed ◽  
Early Stage ◽  
Ct Images ◽  
Patient Identification ◽  
Image Processing Algorithm ◽  
Convolutional Networks ◽  
Model Classification ◽  
Automated Method ◽  
Two Phases

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

scholarly journals A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

2020 ◽  
Author(s):  
Mohammad Rahimzadeh ◽  
Abolfazl Attar ◽  
Seyed Mohammad Sakhaei
Keyword(s):  
Ct Scan ◽  
Early Stage ◽  
Proper Time ◽  
Ct Images ◽  
Image Sequences ◽  
Image Processing Algorithm ◽  
Automated Method ◽  
The World ◽  
Feature Pyramid ◽  
Lung Ct

AbstractCOVID-19 is a severe global problem that has crippled many industries and killed many people around the world. One of the primary ways to decrease the casualties is the infected person’s identification at the proper time. AI can play a significant role in these cases by monitoring and detecting infected persons in early-stage so that it can help many organizations. In this paper, we aim to propose a fully-automated method to detect COVID-19 from the patient’s CT scan without needing a clinical technician. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infection. Our proposed network takes all the CT scan image sequences of a patient as the input and determines if the patient is infected with COVID-19. At the first stage, this network runs an image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This helps to reduce the number of images that shall be identified as normal or COVID-19, so it reduces the processing time. Also, running this algorithm makes the deep network at the next stage to analyze only the proper images and thus reduces false detections. At the next stage, we propose a modified version of ResNet50V2 that is enhanced by a feature pyramid network for classifying the selected CT images into COVID-19 or normal. If enough number of chosen CT scan images of a patient be identified as COVID-19, the network considers that patient, infected to this disease. The ResNet50V2 with feature pyramid network achieved 98.49% accuracy on more than 7996 validation images and correctly identified almost 237 patients from 245 patients.

An Automated Method for Detecting the Scar Tissue in the Left Ventricular Endocardial Wall Using Deep Learning Approach

2020 ◽  
Vol 16 (3) ◽  
pp. 206-213
Author(s):  
Yashbir Singh ◽  
Deepa Shakyawar ◽  
Weichih Hu
Keyword(s):  
Deep Learning ◽  
Cardiac Ct ◽  
Scar Tissue ◽  
Ct Images ◽  
Left Ventricular ◽  
Learning Approach ◽  
Morphological Operations ◽  
Image Evaluation ◽  
Automated Method ◽  
Pixel Value

Background: Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. Objective: To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. Method: Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. Result: We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. Conclusion: Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.

Sign in / Sign up

Export Citation Format

Share Document