2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)最新文献_第8页

Level Set Based on Brain Radiological Densities for Stroke Segmentation in CT Images 基于脑放射密度水平集的CT图像脑划分割

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.172

Elizângela de S. Rebouças, Alan M. Braga, R. Sarmento, R. Marques, P. Filho

{"title":"Level Set Based on Brain Radiological Densities for Stroke Segmentation in CT Images","authors":"Elizângela de S. Rebouças, Alan M. Braga, R. Sarmento, R. Marques, P. Filho","doi":"10.1109/CBMS.2017.172","DOIUrl":"https://doi.org/10.1109/CBMS.2017.172","url":null,"abstract":"Cardiovascular diseases (CVD) are the leading cause of death worldwide, and every year more people die of these diseases. Aiming to assist medical diagnoses through Computerized Tomography (CT) scans, this work proposes a new approach to segment CT images of the brain damaged by stroke. The proposed method takes into account two improvements of the level set method based on the likelihood of Normal distribution. The first improvement is to handle the grayscale image input according to a range analysis of the image intensity scale, adopting 80 HU for the window width and 40 HU for the center level. In addition, we propose an optimal level set initialization, where the zero level set is determined by analyzing the brain density. These improvements to the level set method generate efficient stroke segmentation in CT images of the brain. The results of the proposed method are compared against those of the level set algorithm based on the coherent propagation method, and also those from the Watershed and Region Growing algorithms using a ground truth built by a specialist. The experimental results show that the proposed method presents superior performance, and that it is a promising tool to assist medical diagnoses.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122292097","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: 14

Weightbit: An Advancement in Wearable Technology Weightbit:可穿戴技术的进步

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.85

Dario Guida, A. Basukoski, Performance Database

{"title":"Weightbit: An Advancement in Wearable Technology","authors":"Dario Guida, A. Basukoski, Performance Database","doi":"10.1109/CBMS.2017.85","DOIUrl":"https://doi.org/10.1109/CBMS.2017.85","url":null,"abstract":"Wearable devices are becoming an important interface between users and fitness activities. Their capabilities are improving exponentially, and new strategies are being developed to track sports using sensors that are widely used in robotics. These wearable gadgets are normally created in conjunction with smartphone applications enabling the user to visualise the data and share it through social networks, or compete with other users. The technology behind these devices is often simple using sensors that can be found in a smartphone, such as GPS, accelerometer and gyroscope. However, there are currently no devices capable of measuring the gym activity of weight lifting. In this paper, we present WeightBit: a system consisting of technologically enhanced gym gloves, comprised of the aforementioned sensors as well as an additional weight sensor to detect weight and arm movements. Using this data in combination with a smartphone application, it will be possible to monitor a new series of sports activities with specific focus on weight training. Furthermore, the data collected by the application will enable broader research by medical researchers or institutions. The goal is to keep users focused and keen to live a healthy life, providing them a great tool to track their progress, and to develop a system that will allow medical institutions access to this data for further study.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127313511","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

Automatic Pigment Network Classification Using a Combination of Classical Texture Descriptors and CNN Features 结合经典纹理描述符和CNN特征的色素网络自动分类

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.63

Melinda Pap, B. Harangi, A. Hajdu

{"title":"Automatic Pigment Network Classification Using a Combination of Classical Texture Descriptors and CNN Features","authors":"Melinda Pap, B. Harangi, A. Hajdu","doi":"10.1109/CBMS.2017.63","DOIUrl":"https://doi.org/10.1109/CBMS.2017.63","url":null,"abstract":"The presence of atypical (irregular) pigment networks can be a symptom of melanoma malignum in skin lesions, thus, their proper recognition is a critical task. For object classification problems, the application of deep convolutional neural nets (CNN) receives priority attention nowadays for their high recognition rate. The descriptive features found by CNNs are more hidden than the classically applied ones for texture recognition. In this paper, we investigate whether CNN features outperform the classical texture descriptors in the classification of typical/atypical pigment network. Beyond performing this analysis, we have also found that the aggregation of CNN and classical features within a joint classification framework had a superior performance. Specifically, the union of the CNN and classical feature sets leads to a much higher stability in classification performance for various classifiers. As for quantitative figures, we have reached 90.44% recognition accuracy using a specific subset of this combined feature set obtained by linear forward feature selection and using a Bayes Net as classifier.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133235426","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: 2

ACESO: Analysis of Cervical Cancer: An Evidence-Based Treatments Optimization ACESO:分析宫颈癌:循证治疗优化

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.166

P. Katrakazas, Marilena Tarousi, K. Giokas, D. Koutsouris

引用次数: 0

Robotic Applications Towards an Interactive Alerting System for Medical Purposes 机器人在交互式医疗报警系统中的应用

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.17

K. Panayiotou, Sofia E. Reppou, G. Karagiannis, E. Tsardoulias, Aristeidis G. Thallas, A. Symeonidis

引用次数: 0

On Supporting Parkinson's Disease Patients: The i-Prognosis Personalized Game Suite Design Approach 关于支持帕金森病患者:i-Prognosis个性化游戏套件设计方法

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.144

S. Dias, E. Konstantinidis, J. Diniz, P. Bamidis, V. Charisis, S. Hadjidimitriou, M. Stadtschnitzer, Peter Fagerberg, Ioannis Ioakeimidis, K. Dimitropoulos, N. Grammalidis, L. Hadjileontiadis

{"title":"On Supporting Parkinson's Disease Patients: The i-Prognosis Personalized Game Suite Design Approach","authors":"S. Dias, E. Konstantinidis, J. Diniz, P. Bamidis, V. Charisis, S. Hadjidimitriou, M. Stadtschnitzer, Peter Fagerberg, Ioannis Ioakeimidis, K. Dimitropoulos, N. Grammalidis, L. Hadjileontiadis","doi":"10.1109/CBMS.2017.144","DOIUrl":"https://doi.org/10.1109/CBMS.2017.144","url":null,"abstract":"The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games). From the methodological point of view, to facilitate the visualization of 14 game-scenarios, the system interface and the PD contexts, the storyboarding technique was adopted here. Overall, the realization of the PGS sets the basis for establishing a holistic framework that could aim at improving motor and non-motor symptoms, in order to inform health care providers and policy makers for its inclusion in routine management for PD.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115401452","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: 12

Neural Networks Modelling after Myocardial Infarction in Rats 大鼠心肌梗死后神经网络建模

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.125

I. Iliopoulou, I. Mourouzis, G. Lambrou, C. Pantos, D. Iliopoulou, D. Koutsouris

{"title":"Neural Networks Modelling after Myocardial Infarction in Rats","authors":"I. Iliopoulou, I. Mourouzis, G. Lambrou, C. Pantos, D. Iliopoulou, D. Koutsouris","doi":"10.1109/CBMS.2017.125","DOIUrl":"https://doi.org/10.1109/CBMS.2017.125","url":null,"abstract":"Cardiac function is reduced after acute myocardial infarction due to myocardial injury and to changes in the viable non-ischemic myocardium, a process known as cardiac remodeling. Current treatment of patients with acute myocardial infarction (AMI) reduces infarct size, preserves left ventricular function, and improves survival. However, it does not prevent remodeling which leads to heart failure. The aim of the present study was to model the echocardiographically estimated data with respect to the surgically collected data using Neural Networks. In particular, we attempted to analyze the relationship between cardiac remodeling variables obtained from echo and the infarct variables obtained from surgical data using neural networks. Towards that purpose, 199 rats were separated in two groups. The first group was subjected to coronary artery ligation, while the second underwent a sham operation. Echocardiography was used for rat monitoring. Scar weight and area were estimated after surgical incision. It appeared that several factors could be modelled with neural networks. Such modeling approaches could be developed to enable the simulation of the pathophysiological process after an Acute Myocardial Infarction (AMI) and predict with accuracy the effects of novel or current treatments that act via modulation of tissue injury, Left Ventricular dilation, geometry and hypertrophy.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116297412","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

Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information 腹主动脉瘤的预后:一种融合临床、形态、生物力学和纹理信息的机器学习方法

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.158

F. García-García, E. Metaxa, S. Christodoulidis, M. Anthimopoulos, N. Kontopodis, Martina Correa-Londono, T. Wyss, Y. Papaharilaou, C. Ioannou, H. Tengg-Kobligk, S. Mougiakakou

{"title":"Prognosis of Abdominal Aortic Aneurysms: A Machine Learning-Enabled Approach Merging Clinical, Morphometric, Biomechanical and Texture Information","authors":"F. García-García, E. Metaxa, S. Christodoulidis, M. Anthimopoulos, N. Kontopodis, Martina Correa-Londono, T. Wyss, Y. Papaharilaou, C. Ioannou, H. Tengg-Kobligk, S. Mougiakakou","doi":"10.1109/CBMS.2017.158","DOIUrl":"https://doi.org/10.1109/CBMS.2017.158","url":null,"abstract":"An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac. A cohort of n=38 patients underwent a baseline examination, plus a follow-up visit to measure AAA growth rates, in terms of its maximum diameter (Dmax) divided by the elapsed time period. Subsequently, each case was labelled as slow, medium or quick growth, compared to the expected rate reported in demographic studies, as a function of gender and baseline Dmax. We computed a total of 102 features (5 clinical, 17 morphometric, 4 biomechanical, and 76 on texture) and used a number of machine learning (ML) algorithms; with the aim of minimizing misclassification costs. The performance of the system was evaluated with a leave-one-out cross-validation scheme. The results achieved by the best performing approach, an ensemble of decision trees (LPBoost) using the entire 102-dimensional feature space, indicated that the combination of different information sources, along with ML algorithms, may have a positive impact on the AAA prognosis assessment.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126427120","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: 2

Differential Diagnosis Listing as Relevance Feedback: An Essential User Interface for Clinical Decision Support Systems 鉴别诊断列表作为相关反馈:临床决策支持系统的基本用户界面

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.92

T. Okumura, T. Kajiyama, N. Sonehara

引用次数: 3

Memorandum: A Mobile App for Efficient Note Keeping in Concurrent Multi-participant Human Subject Studies 备忘录:一个移动应用程序，用于有效的笔记保存并发多参与者人类受试者研究

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.147

Leandros Stefanopoulos, C. Maramis, I. Moulos, I. Ioakimidis, N. Maglaveras

引用次数: 1