International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering最新文献_第2页

Analysis of Finger Thermoregulation by Using Signal Processing Techniques 基于信号处理技术的手指体温调节分析

International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering Pub Date : 2019-01-01 DOI: 10.1007/978-3-030-17935-9_48

María Camila Henao Higuita, Macheily Hernández Fernández, Delio Aristizabal Martínez, Hermes Fandiño Toro

引用次数: 0

Data Fusion for Improving Sleep Apnoea Detection from Single-Lead ECG Derived Respiration 数据融合改善单导联心电图衍生呼吸的睡眠呼吸暂停检测

International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering Pub Date : 2019-01-01 DOI: 10.1007/978-3-030-17935-9_5

Ana Jiménez Martín, Alejandro Cuevas Notario, J. G. García Domínguez, Sara García Villa, Miguel A. Herrero Ramiro

引用次数: 0

Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses 无对齐z曲线基因组倒谱系数和病毒分类的机器学习

International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering Pub Date : 2018-03-07 DOI: 10.1007/978-3-319-78723-7_25

E. Adetiba, O. Olugbara, Tunmike B. Taiwo, M. Adebiyi, J. Badejo, Matthew B. Akanle, V. O. Matthews

引用次数: 3

Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images. 冠状动脉内光学相干断层成像中生物可吸收血管支架支撑物的自动分割。

International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering Pub Date : 2017-10-01 Epub Date: 2018-01-11 DOI: 10.1109/BIBE.2017.00-38

Junedh M Amrute, Lambros Athanasiou, Farhad Rikhtegar, José M de la Torre Hernández, Tamara García Camarero, Elazer R Edelman

{"title":"Automated Segmentation of Bioresorbable Vascular Scaffold Struts in Intracoronary Optical Coherence Tomography Images.","authors":"Junedh M Amrute, Lambros Athanasiou, Farhad Rikhtegar, José M de la Torre Hernández, Tamara García Camarero, Elazer R Edelman","doi":"10.1109/BIBE.2017.00-38","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-38","url":null,"abstract":"Bioresorbable vascular scaffolds (BVS), the next step in the continuum of minimally invasive vascular interventions present new opportunities for patients and clinicians but challenges as well. As they are comprised of polymeric materials standard imaging is challenging. This is especially problematic as modalities like optical coherence tomography (OCT) become more prevalent in cardiology. OCT, a light-based intracoronary imaging technique, provides cross-sectional images of plaque and luminal morphology. Until recently segmentation of OCT images for BVS struts was performed manually by experts. However, this process is time consuming and not tractable for large amounts of patient data. Several automated methods exist to segment metallic stents, which do not apply to the newer BVS. Given this current limitation coupled with the emerging popularity of the BVS technology, it is crucial to develop an automated methodology to segment BVS struts in OCT images. The objective of this paper is to develop a novel BVS strut detection method in intracoronary OCT images. First, we preprocess the image to remove imaging artifacts. Then, we use a K-means clustering algorithm to automatically segment the image. Finally, we isolate the stent struts from the rest of the image. The accuracy of the proposed method was evaluated using expert estimations on 658 annotated images acquired from 7 patients at the time of coronary arterial interventions. Our proposed methodology has a positive predictive value of 0.93, a Pearson Correlation coefficient of 0.94, and a F1 score of 0.92. The proposed methodology allows for rapid, accurate, and fully automated segmentation of BVS struts in OCT images.","PeriodicalId":89775,"journal":{"name":"International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering","volume":"2017 ","pages":"297-302"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBE.2017.00-38","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36432547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A MR Brain Classification Method Based on Multiscale and Multiblock Fuzzy C-means. 基于多尺度多块模糊c均值的MR脑分类方法。

International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering Pub Date : 2011-01-01 DOI: 10.1109/icbbe.2011.5780357

Xiaofeng Yang, Baowei Fei

{"title":"A MR Brain Classification Method Based on Multiscale and Multiblock Fuzzy C-means.","authors":"Xiaofeng Yang, Baowei Fei","doi":"10.1109/icbbe.2011.5780357","DOIUrl":"10.1109/icbbe.2011.5780357","url":null,"abstract":"A fully automatic, multiscale and multiblock fuzzy C-means (MsbFCM) classification method with intensity correction for MR images is presented in this paper. We use a bilateral filter to process MR images and to build a multiscale image series by increasing the standard deviation of spatial function and reducing the standard deviation of range function. We separate every scale image into multiple blocks and for every block a multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels to overcome the effect of intensity inhomogeneity. The method is robust for noise MR images with intensity inhomogeneity because of its multiscale and multiblock bilateral filtering scheme. Our method was compared with the conventional FCM, a modified FCM (MFCM) and multiscale FCM (MsFCM) method on synthesized images, simulated brain MR images, and real MR images. The MsbFCM method achieved an overlap ratio of greater than 91% as validated by the ground truth even if original images have 9% noise and 40% intensity inhomogeneity. Experimental results using real MR images demonstrate the effectiveness of the proposed method. Our MsbFCM classification method is accurate and robust for various MR images.","PeriodicalId":89775,"journal":{"name":"International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering","volume":" ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552386/pdf/nihms432047.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31194977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictor Profile to assess Recurrence Phenomenon of Suicidal Incidents among Region Based Target Population. 评估自杀事件在区域目标人群中的复发现象的预测因子谱。

International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering Pub Date : 2009-06-01 DOI: 10.1109/ICBBE.2009.5163217

V Vatsalya, R Karch, K Chandras, S Srivastava, Roop Sunil Reddy

{"title":"Predictor Profile to assess Recurrence Phenomenon of Suicidal Incidents among Region Based Target Population.","authors":"V Vatsalya, R Karch, K Chandras, S Srivastava, Roop Sunil Reddy","doi":"10.1109/ICBBE.2009.5163217","DOIUrl":"10.1109/ICBBE.2009.5163217","url":null,"abstract":"Suicidal attempts have a very significant effect on the society, and they also reflect on the efforts of the supporting health care and counseling facilities and the therapist involved. The impact of suicide is further magnified by the needs of persons who attempt suicide multiple times, requiring emergency health care and rehabilitation. Preventing such activities becomes a major task for the support providing agencies as soon as clients with such tendencies are identified. There are repetitive traits that can be observed during the therapeutic program among the high-risk group individuals, who are susceptible to this kind of activity and such traits indicate for a specific profile. The aim of the instrument is to prevent the occurrence of the repetitive suicidal attempts of the clients in Washington State, which has one of the highest suicide rates. This profile has been constructed on the various parameters recognized in the statistical analysis of the client pool in Washington State, who have been under treatment for their suicidal behavior. This predictor indicates the probability of clients who attempt suicides repetitively by matching the parameters of the profile with that of the client. Building a profile for the purpose of predicting behavior of this kind can strengthen the intervention strategies more comprehensively and reduce such incidents and health care requirements and expenses.","PeriodicalId":89775,"journal":{"name":"International Conference on Bioinformatics and Biomedical Engineering : [proceedings]. International Conference on Bioinformatics and Biomedical Engineering","volume":"2009 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2009-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4365938/pdf/nihms670471.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33152068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0