Int. J. Medical Eng. Informatics最新文献_第8页

Toward a patient-centred and pattern-based workflow for interoperable hospital information systems 为可互操作的医院信息系统建立以患者为中心和基于模式的工作流程

Int. J. Medical Eng. Informatics Pub Date : 2017-07-12 DOI: 10.1504/IJMEI.2017.10004452

Abdelhak Kaddari, M. Malki, Said Benomar Elmdeghri

{"title":"Toward a patient-centred and pattern-based workflow for interoperable hospital information systems","authors":"Abdelhak Kaddari, M. Malki, Said Benomar Elmdeghri","doi":"10.1504/IJMEI.2017.10004452","DOIUrl":"https://doi.org/10.1504/IJMEI.2017.10004452","url":null,"abstract":"At the time, the proper management of business processes is considered among the potential points that make up the business development debate. Workflow management with service composition in a business process is one of the levers to achieve this goal by acting on the modelling techniques. The adoption of web services platform in medical field did not attain good success. For from the organisational point of view, it is a prerequisite to find a good mechanism that allows the establishment of a good correspondence between the medical activities performed and the functionalities of related web services. We proposed in this paper, the automation approach based on a medical activity reference pattern. This approach includes two interesting steps: the first is presented by automation workflow of the planning task of medical process to be performed on each patient. The second is presented by automation workflow of implementation and management of planned medical processes. We used different business process management (BPM) technologies to achieve the implementation of an intermediate layer between cooperative systems. Our proposal ensures proper organisation of cooperative work and reinforces action's traceability for each stakeholder.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215054","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

Enhancement and segmentation of pituitary gland from MR brain images 脑磁共振图像中垂体的增强与分割

Int. J. Medical Eng. Informatics Pub Date : 2017-07-12 DOI: 10.1504/IJMEI.2017.10004446

S. A. Banday, A. H. Mir

引用次数: 0

Brain tumour segmentation and detection using modified region growing and genetic algorithm in MRI images 基于改进区域生长和遗传算法的MRI图像脑肿瘤分割与检测

Int. J. Medical Eng. Informatics Pub Date : 2017-07-12 DOI: 10.1504/IJMEI.2017.10004454

A. Kavitha, C. Chellamuthu

{"title":"Brain tumour segmentation and detection using modified region growing and genetic algorithm in MRI images","authors":"A. Kavitha, C. Chellamuthu","doi":"10.1504/IJMEI.2017.10004454","DOIUrl":"https://doi.org/10.1504/IJMEI.2017.10004454","url":null,"abstract":"In modern medical research several approaches of image segmentation are used for the detection of brain tumour. Several pieces of medical equipment such as magnetic resonance imaging (MRI) scans, X-ray and computed tomography (CT) are used for diagnosis of brain tumour. This paper proposes a new segmentation method which combines modified region growing and genetic algorithm for detecting brain tumour. This consists of four steps-pre-processing, segmentation, classification and fitness calculation. Pre-processing uses Gaussian filter for removal of noise present in the image. The pre-processed image is segmented using modified region growing (MRG) method which includes the orientation constraint in addition to the intensity constraint used in region growing (RG) method. Back propagation neural network (BPNN) classifier classifies the tumour as normal or abnormal. Then a genetic approach of initial population and fitness calculation is done to find the optimum value for the best segmented tumour portion of the MRI image. The proposed approach overcomes dark abnormalities as well as over segmentation problem. Implementing the proposed method on MRI image helps in creating awareness to patients and it also serves as a perquisite for Radiologists, doctors in rural areas for providing effective treatment to the brain tumour patients.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127116305","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

Cardiac signal enhancement using normalised variable step algorithm for remote healthcare monitoring systems 用于远程医疗监测系统的归一化变步长算法心脏信号增强

Int. J. Medical Eng. Informatics Pub Date : 2017-03-24 DOI: 10.1504/IJMEI.2017.10002622

Md. Nizamuddin Salman, P. Rao, Muhammad Zia Ur Rahman

引用次数: 11

Gait signal classification using an in-house built goniometer and naïve Bayes classifier 使用内部构建的角计和naïve贝叶斯分类器进行步态信号分类

Int. J. Medical Eng. Informatics Pub Date : 2017-03-24 DOI: 10.1504/IJMEI.2017.10002621

R. Khnouf, E. Abdulhay, Rawan Al Junaidi, F. Rifai

引用次数: 0

Recognition of emotional states in response to audio-visual inductions based on nonlinear analysis and self-organisation map classification 基于非线性分析和自组织映射分类的视听感应情绪状态识别

Int. J. Medical Eng. Informatics Pub Date : 2017-03-24 DOI: 10.1504/IJMEI.2017.10002606

S. Hatamikia, A. Nasrabadi

{"title":"Recognition of emotional states in response to audio-visual inductions based on nonlinear analysis and self-organisation map classification","authors":"S. Hatamikia, A. Nasrabadi","doi":"10.1504/IJMEI.2017.10002606","DOIUrl":"https://doi.org/10.1504/IJMEI.2017.10002606","url":null,"abstract":"Recently, emotion recognition using biological signals has attracted much attention by researchers due to the rapid development of machine learning algorithms and various applications of brain computer interface (BCI). This study addresses the emotion recognition system from electroencephalogram signals, in which different emotional states are represented on the valence and arousal dimensions. As regards to nonlinear nature and complex dynamics of EEG signals, we propose to use nonlinear features from brain electrical activity to evaluate emotional states. With this aim, we examined two different categories of nonlinear features: fractal-based features and entropy-based features. In addition to that, a two stage feature selection based on Dunn index and sequential forward feature selection (SFS) algorithm is employed for eliminating redundant and weak features, and finally SOM classifier was applied to selected features in order to classification of emotional classes. The experimental results show that the proposed method can represent user's emotional state effectively in both two-level and four-level of valence and arousal dimension. Furthermore, we determined the best channels and time segments for discriminating the emotions and the most related regions of brain to emotion-related sensory activities were found.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114090255","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

Control the tumour growth via sliding mode control 通过滑模控制控制肿瘤生长

Int. J. Medical Eng. Informatics Pub Date : 2017-03-24 DOI: 10.1504/IJMEI.2017.10002633

A. P. Shahri, S. Haghighatnia, R. Moghaddam, H. Kobravi

引用次数: 3

Developing a fuzzy OWL ontology for obesity related cancer domain 肥胖症相关癌症领域模糊OWL本体的开发

Int. J. Medical Eng. Informatics Pub Date : 2017-03-24 DOI: 10.1504/IJMEI.2017.10002627

M. A. Elhefny, M. Elmogy, Ahmed Abou El-Fetouh, F. Badria

引用次数: 3

Clinical decision support system as a risk assessment tool to aid in earlier diagnosis of pancreatic cancer 临床决策支持系统作为胰腺癌早期诊断的风险评估工具

Int. J. Medical Eng. Informatics Pub Date : 2017-03-24 DOI: 10.1504/IJMEI.2017.10002620

E. Adams, D. Mital, Shashi Mehta

引用次数: 1

Comparison of muscle activation patterns among healthy males and females during different lower limb movements 不同下肢运动时健康男性和女性肌肉激活模式的比较

Int. J. Medical Eng. Informatics Pub Date : 2016-09-29 DOI: 10.1504/IJMEI.2016.079357

S. Mathur, Manvinder Kaur, D. Bhatia, S. Verma

引用次数: 0