2019 IEEE International Conference on Imaging Systems and Techniques (IST)最新文献_第7页

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2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/ist48021.2019.9010232

引用次数: 0

Evaluation of Oversampling Strategies in Machine Learning for Space Debris Detection 空间碎片检测机器学习中过采样策略的评价

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010217

M. Khalil, E. Fantino, P. Liatsis

{"title":"Evaluation of Oversampling Strategies in Machine Learning for Space Debris Detection","authors":"M. Khalil, E. Fantino, P. Liatsis","doi":"10.1109/IST48021.2019.9010217","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010217","url":null,"abstract":"In recent years, the number of resident space objects has increased dramatically. The chances of space objects colliding with each other are increasing, thus posing a threat to active satellites and future space missions. Identifying and detecting space debris is essential in ensuring the security of space assets. In this contribution, we investigate the effectiveness of several feature extraction and oversampling techniques by attempting classification of real-world light curves of space objects using eight machine learning methods. Three feature extraction tools are utilized to represent the light curves as sets of features, i.e., FATS (Feature Analysis for Time Series), feets (feATURE eXTRACTOR FOR tIME sERIES) and UPSILoN (AUtomated Classification for Periodic Variable Stars using MachIne LearNing) public tools. To address the problem of class imbalance, four oversampling techniques are applied, i.e., ADaptive SYNthetic Sampling Approach (ADASYN), Synthetic Minority Oversampling TEchnique (SMOTE), and two modifications of SMOTE, specifically, Borderline-SMOTE and Support Vector Machine (SVM)-SMOTE. Results show that the features extracted using the FATS tool lead to a better performance, and therefore, they appear to represent light curves in a more informative manner, compared to feets and UPSILoN. Moreover, the use of SVM-SMOTE technique improves the performance of the utilized classifiers more than other oversampling techniques.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122904282","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: 3

A CNN-Based Framework for Automatic Vitreous Segemntation from OCT Images 基于cnn的OCT图像玻璃体自动分割框架

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010133

S. Hagagg, F. Khalifa, H. Abdeltawab, A. Elnakib, M. Abdelazim, M. Ghazal, H. Sandhu, A. El-Baz

{"title":"A CNN-Based Framework for Automatic Vitreous Segemntation from OCT Images","authors":"S. Hagagg, F. Khalifa, H. Abdeltawab, A. Elnakib, M. Abdelazim, M. Ghazal, H. Sandhu, A. El-Baz","doi":"10.1109/IST48021.2019.9010133","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010133","url":null,"abstract":"Accurate segmentation of the vitreous region of retinal images is an essential step in any computer-aided diagnosis system for severity grading of vitreous inflammation. In this paper, we developed a framework to automatically segment the vitreous region from optical coherence tomography (OCT) images of uveitis eyes using fully convolutional neural network (CNN), U-net model. The CNN model consists of a contracting path to capture context and an expanding path for precise localization and utilizes the binary cross entropy (BCE) loss. The model has been tested on 200 OCT scans of eyes having different grades of uveitis severity (0–4). The developed CNN model demonstrated not only high accuracy of vitreous segmentation, documented by two evaluation metrics (Dice coefficient (DC) and Hausdorff distance (HD) are 0.94 ± 0.13 and 0.036 mm ± 0.086 mm, respectively), but also requires a small number of images for training. In addition, the training process of the model converges in few iterations, affording fast speed contrary to what is expected in such cases of deep learning problems. These preliminary results show the promise of the proposed CNN for accurate segmentation of the vitreous region from retinal OCT images.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133282483","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: 4

Renyi divergence and non-deterministic subsampling in Rigid Image Registration 刚性图像配准中的Renyi散度和非确定性子采样

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010237

C. Spanakis, E. Mathioudakis, N. Kampanis, M. Tsiknakis, K. Marias

引用次数: 1

Simulation of Flooding Phenomenon in Packed Column using Electrical Capacitance Tomography 用电容层析成像技术模拟填料柱中泛洪现象

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010204

Yuan Chen, Zhigang Li, Yunjie Yang, J. Jia, Chang Liu, M. Lucquiaud

{"title":"Simulation of Flooding Phenomenon in Packed Column using Electrical Capacitance Tomography","authors":"Yuan Chen, Zhigang Li, Yunjie Yang, J. Jia, Chang Liu, M. Lucquiaud","doi":"10.1109/IST48021.2019.9010204","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010204","url":null,"abstract":"Packed column flooding is caused by an excessive liquid enters a packed column, leading to a significant increase in pressure drop, liquid hold-up, and loss in separation efficiency. In this simulation study, electrical capacitance tomography (ECT) is used to monitor the flooding phenomenon in a packed column by inferring the liquid hold-up from the reconstructed images. A simulation study is implemented to reconstruct the liquid phase distributions across the structured packed column by using ECT during the flooding process. The flooding phenomenon is simulated by adding a different amount of water droplets from the bottom of the packed column to above. The capacitances measured using the ECT sensor are then used to reconstruct the images of the liquid phase distributions. A significant difference in the liquid phase distributions and the liquid hold-up can be observed during the flooding region. Simulation results demonstrate that ECT is capable of monitoring and further predicting the flooding phenomenon with high-fidelity liquid phase distribution image and the calculated liquid hold-up.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114808588","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: 1

Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy 基于深度学习的糖尿病视网膜病变计算机辅助诊断方法

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010333

Omar Dekhil, A. Naglah, M. Shaban, M. Ghazal, F. Taher, A. Elbaz

{"title":"Deep Learning Based Method for Computer Aided Diagnosis of Diabetic Retinopathy","authors":"Omar Dekhil, A. Naglah, M. Shaban, M. Ghazal, F. Taher, A. Elbaz","doi":"10.1109/IST48021.2019.9010333","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010333","url":null,"abstract":"Diabetic retinopathy (DR) is a retinal disease caused by the high blood sugar levels that may damage and block the blood vessels feeding the retina. In the early stages of DR, the disease is asymptomatic; however, as the disease advances, a possible sudden loss of vision and blindness may occur. Therefore, an early diagnosis and staging of the disease is required to possibly slow down the progression of the disease and improve control of the symptoms. In response to the previous challenge, we introduce a computer aided diagnosis tool based on convolutional neural networks (CNN) to classify fundus images into one of the five stages of DR. The proposed CNN consists of a preprocessing stage, five stage convolutional, rectified linear and pooling layers followed by three fully connected layers. Transfer learning was adopted to minimize overfitting by training the model on a larger dataset of 3.2 million images (i.e. ImageNet) prior to the use of the model on the APTOS 2019 Kaggle DR dataset. The proposed approach has achieved a testing accuracy of 77% and a quadratic weighted kappa score of 78%, offering a promising solution for a successful early diagnose and staging of DR in an automated fashion.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126754927","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: 26

Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning 使用无监督机器学习识别乳腺癌遗传特征模式

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010510

R. Hamoudi, M. Bettayeb, Areej Alsaafin, M. Hachim, Q. Nasir, A. B. Nassif

引用次数: 4

Frequency-difference imaging for multi-frequency complex-valued ECT 多频复值ECT的频差成像

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010531

Haokun Wang, Maomao Zhang, Yunjie Yang

引用次数: 1

Fractional Order Savitzky-Golay Differentiator based Approach for Mammogram Enhancement 基于分数阶Savitzky-Golay微分器的乳房x线增强方法

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010231

K. K. Singh, M. Bajpai

引用次数: 2

A Machine Learning Approach for Grading Autism Severity Levels Using Task-based Functional MRI 一种基于任务的功能性MRI的自闭症严重程度分级的机器学习方法

2019 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2019-12-01 DOI: 10.1109/IST48021.2019.9010335

Reem T. Haweel, Omar Dekhil, A. Shalaby, Ali H. Mahmoud, M. Ghazal, R. Keynton, G. Barnes, A. El-Baz

{"title":"A Machine Learning Approach for Grading Autism Severity Levels Using Task-based Functional MRI","authors":"Reem T. Haweel, Omar Dekhil, A. Shalaby, Ali H. Mahmoud, M. Ghazal, R. Keynton, G. Barnes, A. El-Baz","doi":"10.1109/IST48021.2019.9010335","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010335","url":null,"abstract":"Autism is a developmental disorder associated with difficulties in communication and social interaction. Autism diagnostic observation schedule (ADOS) is considered the gold standard in autism diagnosis, which estimates a score explaining the severity level for each individual. Currently, brain image modalities are being investigated for the development of objective technologies to diagnose Autism spectrum disorder (ASD). Alterations in functional activity is believed to be important in explaining autism causative factors. This paper presents a machine learning approach for grading severity level of the autistic subjects using task-based functional MRI data. The local features related to the functional activity of the brain is obtained from a speech experiment. According to ADOS reports, the adopted dataset is classified to three groups: Mild, moderate and severe. Our analysis is divided into two parts: (i) individual subject analysis and (ii) higher level group analysis. We use the individual analysis to extract the features used in classification, while the higher level analysis is used to infer the statistical differences between groups. The obtained classification accuracy is 78% using the random forest classifier.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134104073","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: 8