Journal of Medical Imaging and Health Informatics最新文献

Medical Imaging and Health Informatics 医学成像和健康信息学

Journal of Medical Imaging and Health Informatics Pub Date : 2022-06-14 DOI: 10.1002/9781119819165

T. Jaware, K. Kumar, R. Badgujar, S. Antonov

引用次数: 0

Volume Subtraction Method Using Dual Reconstruction and Additive Technique for Pulmonary Artery/Vein 3DCT Angiography 双重建加性技术在肺动脉/静脉3DCT血管造影中的体积减影方法

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.394

Yoshinori Tanabe, Yuka Tanaka, H. Nagata, Reina Murayama, T. Ishida

{"title":"Volume Subtraction Method Using Dual Reconstruction and Additive Technique for Pulmonary Artery/Vein 3DCT Angiography","authors":"Yoshinori Tanabe, Yuka Tanaka, H. Nagata, Reina Murayama, T. Ishida","doi":"10.1166/jmihi.2022.394","DOIUrl":"https://doi.org/10.1166/jmihi.2022.394","url":null,"abstract":"This study aimed to develop a method for pulmonary artery and vein (PA/PV) separation in three-dimensional computed tomography (3DCT), using a dual reconstruction technique and the addition of CT images. The physical image properties of multiple reconstruction kernels (FC13; FC13 3D-Q03;\u0000 FC30 3D-Q03; FC83; FC13 twofold addition; FC13 threefold addition; FC13 fourfold addition; FC13 [3D-Q03] twofold addition; FC13+FC30 (3D-Q03); FC13+FC83) were evaluated based on spatial resolution using a modulation transfer function. The lung kernel CT image (FC 83) had a high spatial resolution\u0000 with a 10% modulation transfer function (0.847). The noise power spectrum of the additive CT images was measured, and the CT values for the PA/PV with and without addition were compared. The addition of CT images increased the CT values difference between the PA/PV. The PA/PV 3DCT angiography\u0000 (PA/PV 3DCTA), even with a small difference in CT values, could be effectively separated using high spatial resolution kernel CT and the addition of CT images dedicated to subtraction. This novel, simple method could create PA/PV 3DCTA using a general CT scanner and 3D workstation that can\u0000 be easily performed at any facility.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82169168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Machine Learning Based Probabilistic Classification Model for Heart Disease Prediction 一种新的基于机器学习的心脏病预测概率分类模型

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3940

A. Ann Romalt, Mathusoothana S. Kumar

引用次数: 0

A Rapid Dual Feature Tracking Method for Medical Equipments Assembly and Disassembly in Markerless Augmented Reality 无标记增强现实中医疗设备拆装快速双特征跟踪方法

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3944

D. Roopa, S. Bose

{"title":"A Rapid Dual Feature Tracking Method for Medical Equipments Assembly and Disassembly in Markerless Augmented Reality","authors":"D. Roopa, S. Bose","doi":"10.1166/jmihi.2022.3944","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3944","url":null,"abstract":"Markerless Augmented Reality (MAR) is a superior technology that is currently used by the medical device assembler with aid in design, assembly, disassembly and maintenance operations. The medical assembler assembles the medical equipment based on the doctors requirement, they also\u0000 maintains quality and sanitation of the equipment. The major research challenges in MAR are as follows: establish automatic registration parts, find and track the orientation of parts, and lack of depth and visual features. This work proposes a rapid dual feature tracking method i.e., combination\u0000 of Visual Simultaneous Localization and Mapping (SLAM) and Matched Pairs Selection (MAPSEL). The main idea of this work is to attain high tracking accuracy using the combined method. To get a good depth image map, a Graph-Based Joint Bilateral with Sharpening Filter (GRB-JBF with SF) is proposed\u0000 since depth images are noisy due to the dynamic change of environmental factors that affects tracking accuracy. Then, the best feature points are obtained for matching using Oriented Fast and Rotated Brief (ORB) as a feature detector, Fast Retina Key point with Histogram of Gradients (FREAK-HoG)\u0000 as a feature descriptor, and Feature Matching using Rajsk’s distance. Finally, the virtual object is rendered based on 3D affine and projection transformation. This work computes the performance in terms of tracking accuracy, tracking time, and rotation error for different distances\u0000 using MATLAB R2017b. From the observed results, it is perceived that the proposed method attained the least position error value about 0.1 cm to 0.3 cm. Also, rotation error is observed as minimal between 2.40 (Deg) to 3.10 and its average scale is observed as 2.7140. Further, the proposed\u0000 combination consumes less time against frames compared with other combinations and obtained a higher tracking accuracy of about 95.14% for 180 tracked points. The witnessed outcomes from the proposed scheme display superior performance compared with existing methods.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78393018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Multimodal Fusion Based Hyperparameter Tuned Deep Learning Model for Brain Tumor Diagnosis 基于自动多模态融合的超参数调谐深度学习脑肿瘤诊断模型

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3942

S. Sandhya, M. Senthil Kumar

{"title":"Automated Multimodal Fusion Based Hyperparameter Tuned Deep Learning Model for Brain Tumor Diagnosis","authors":"S. Sandhya, M. Senthil Kumar","doi":"10.1166/jmihi.2022.3942","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3942","url":null,"abstract":"As medical image processing research has progressed, image fusion has emerged as a realistic solution, automatically extracting relevant data from many images before fusing them into a single, unified image. Medical imaging techniques, such as Computed Tomography (CT), Magnetic Resonance\u0000 Imaging (MRI), etc., play a crucial role in the diagnosis and classification of brain tumors (BT). A single imaging technique is not sufficient for correct diagnosis of the disease. In case the scans are ambiguous, it can lead doctors to incorrect diagnoses, which can be unsafe to the patient.\u0000 The solution to this problem is fusing images from different scans containing complementary information to generate accurate images with minimum uncertainty. This research presents a novel method for the automated identification and classification of brain tumors using multi-modal deep learning\u0000 (AMDL-BTDC). The proposed AMDL-BTDC model initially performs image pre-processing using bilateral filtering (BF) technique. Next, feature vectors are generated using a pair of pre-trained deep learning models called EfficientNet and SqueezeNet. Slime Mold Algorithm is used to acquire the DL\u0000 models’ optimal hyperparameter settings (SMA). In the end, an autoencoder (AE) model is used for BT classification once features have been fused. The suggested model’s superior performance over other techniques under diverse measures was validated by extensive testing on the benchmark\u0000 medical imaging dataset.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91220021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recurrent Neural Network Deep Learning Techniques for Brain Tumor Segmentation and Classification of Magnetic Resonance Imaging Images 脑肿瘤磁共振成像图像分割与分类的递归神经网络深度学习技术

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3943

Meenal Thayumanavan, Asokan Ramasamy

{"title":"Recurrent Neural Network Deep Learning Techniques for Brain Tumor Segmentation and Classification of Magnetic Resonance Imaging Images","authors":"Meenal Thayumanavan, Asokan Ramasamy","doi":"10.1166/jmihi.2022.3943","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3943","url":null,"abstract":"Brain Tumour is a one of the most threatful disease in the world. It reduces the life span of human beings. Computer vision is advantageous for human health research because it eliminates the need for human judgement to get accurate data. The most reliable and secure imaging techniques\u0000 for magnetic resonance imaging are CT scans, X-rays, and MRI scans (MRI). MRI can locate tiny objects. The focus of our paper will be the many techniques for detecting brain cancer using brain MRI. Early detection of tumour and diagnosis is might essential to radiologist to initiate better\u0000 treatment. MRI is a competent and speedy method of examining a brain tumour. Resonance in Magnetic Fields Imaging technology is a non-invasive technique that aids in the segmentation of brain tumour images. Deep learning algorithm delivers good outcomes in terms of reducing time consumption\u0000 and precise tumour diagnosis (solution). This research proposed that a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Supervised Deep Learning model be used to automatically find and split brain tumours. The RNN Model outperforms the CNN Model by 98.91 percentage. These\u0000 models categorize brain images as normal or pathological, and their performance was evaluated.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88924038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convolutional Neural Network-BO Based Feature Extraction and Multi-Layer Neural Network-SR Based Classification for Facial Expression Recognition 基于卷积神经网络- bo特征提取和基于多层神经网络- sr分类的面部表情识别

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3938

K. Pandikumar, K. Senthamil Selvan, B. Sowmya, A. Niranjil Kumar

{"title":"Convolutional Neural Network-BO Based Feature Extraction and Multi-Layer Neural Network-SR Based Classification for Facial Expression Recognition","authors":"K. Pandikumar, K. Senthamil Selvan, B. Sowmya, A. Niranjil Kumar","doi":"10.1166/jmihi.2022.3938","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3938","url":null,"abstract":"Facial expression recognition has been more essential in artificial machine intelligence systems in recent years. Recognizing facial expressions automatically has constantly been considered as a challenging task since people significantly vary the way of exhibiting their facial expressions.\u0000 Numerous researchers established diverse approaches to analyze the facial expressions automatically but there arise few imprecision issues during facial recognition. To address such shortcomings, our proposed approach recognizes the facial expressions of humans in an effective manner. The\u0000 suggested method is divided into three stages: pre-processing, feature extraction, and classification. The inputs are pre-processed at the initial stage and CNN-BO algorithm is used to extract the best feature in the feature extraction step. Then the extracted feature is provided to the classification\u0000 stage where MNN-SR algorithm is employed in classifying the face expression as joyful, miserable, normal, annoyance, astonished and frightened. Also, the parameters are tuned effectively to obtain high recognition accuracy. In addition to this, the performances of the proposed approach are\u0000 computed by employing three various datasets namely; CMU/VASC, Caltech faces 1999, JAFFE and XM2VTS. The performance of the proposed system is calculated and comparative analysis is made with few other existing approaches and its concluded that the proposed method provides superior\u0000 performance with optimal recognition rate.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88721680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Based Electrocardiogram Signal Analysis for Abnormalities Detection Using Hybrid Cascade Feed Forward Backpropagation with Ant Colony Optimization Technique 基于深度学习的心电图信号分析与蚁群优化技术

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3945

C. Ganesh, B. Sathiyabhama

{"title":"Deep Learning-Based Electrocardiogram Signal Analysis for Abnormalities Detection Using Hybrid Cascade Feed Forward Backpropagation with Ant Colony Optimization Technique","authors":"C. Ganesh, B. Sathiyabhama","doi":"10.1166/jmihi.2022.3945","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3945","url":null,"abstract":"In this paper, a time series data mining models is introduced for analysis of ECG data for prior identification of heart attacks. The ECG data sets extracted from Physionet are simulated in MATLAB. The Data used for model are preprocessed so that missing data are fulfilled. In this\u0000 work cascade feedforward NN which is similar to Multilayer Perceptron (MLP) architecture is proposed along with Swarm Intelligence. A hybrid method combining cascade-Forward NN Classifier and Ant colony optimization is proposed in this paper. The swarm-based intelligence method optimizes the\u0000 weight adjustment of neural network and enhances the convergence behavior. The novelty is on the optimization of the NN parameters for narrowing down the convergence with ACO implementation. Ant colony optimization is used here for choosing the optimized hidden node. The combined use of machine\u0000 learning algorithm with neural network enhances the performance of the system. The performance is evaluated using parameters like True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) respectively. The Improved accuracy of proposed Classifier model raises the\u0000 speed. In addition, the proposed method uses minimum memory. The implementation was done in MATLAB tool. Real time data was used.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80710322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Liver Cancer Detection and Classification Using Raspberry Pi 基于树莓派的肝癌检测与分类

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3941

T. K. R. Agita, M. Moorthi

{"title":"Liver Cancer Detection and Classification Using Raspberry Pi","authors":"T. K. R. Agita, M. Moorthi","doi":"10.1166/jmihi.2022.3941","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3941","url":null,"abstract":"In practical radiology, early diagnosis and precise categorization of liver cancer are difficult issues. Manual segmentation is also a time-consuming process. So, utilizing various methodologies based on an embedded system, we detect liver cancer from abdominal CT images using automated\u0000 liver cancer segmentation and classification. The objective is to categorize CT scan images of primary and secondary liver disease using a Back Propagation Neural Network (BPNN) classifier, which has greater accuracy than previous approaches. In this work, a newly proposed method is shown\u0000 which has four phases: image preprocessing, image segmentation, extraction of the features, and classification of the liver. Level set segmentation for segmenting the liver from abdominal CT images and Practical Swarm Optimization (PSO) for the tumor segmentation. Then the features from the\u0000 liver are extracted and given to the BPNN classifier to classify the liver cancer. These algorithms are implemented on the Raspberry Pi. Then it serially interfaces with the MAX3232 protocol via serial communication. The GSM 800C module is connected to the system to send SMS as primary or\u0000 secondary cancer. The BPNN classification technique achieved an excellent accuracy of 97.98%. The experimental results demonstrate the efficiency of this proposed approach, which provides excellent accuracy with good results.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"27 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77983768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Fundus Images Using Convolutional Neural Networks 基于卷积神经网络的眼底图像分类

Journal of Medical Imaging and Health Informatics Pub Date : 2022-03-01 DOI: 10.1166/jmihi.2022.3947

R. Sabitha, G. Ramani

{"title":"Classification of Fundus Images Using Convolutional Neural Networks","authors":"R. Sabitha, G. Ramani","doi":"10.1166/jmihi.2022.3947","DOIUrl":"https://doi.org/10.1166/jmihi.2022.3947","url":null,"abstract":"Diabetes causes damage to the retinal blood vessel networks, resulting in Diabetic Retinopathy (DR). This is a serious vision-threatening condition for most diabetics. Color fundus photographs are utilized to diagnose DR, which necessitates the employment of qualified clinicians to\u0000 detect the presence of lesions. It is difficult to identify DR in an automated method. Feature extraction is quite important in terms of automated sickness detection. Convolutional Neural Network (CNN) exceeds previous handcrafted feature-based image classification algorithms in terms of picture\u0000 classification efficiency in the current environment. In order to improve classification accuracy, this work presents the CNN structure for extracting attributes from retinal fundus images. The output properties of CNN are given as input to different machine learning classifiers in this recommended\u0000 strategy. This approach is evaluating using pictures from the EYEPACS datasets using Decision stump, J48 and Random Forest classifiers. To determine the effectiveness of a classifier, its accuracy, false positive rate (FPR), True positive Rate (TPR), precision, recall, F-measure, and Kappa-score\u0000 are illustrated. The recommended feature extraction strategy paired with the Random forest classifier outperforms all other classifiers on the EYEPACS datasets, with average accuracy and Kappa-score (k-score) of 99% and 0.98 respectively.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90018205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0