2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)最新文献

Deep Convolutional Approach for Low-Dose CT Image Noise Reduction 基于深度卷积的低剂量CT图像降噪方法

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430255

S. Badretale, F. Shaker, P. Babyn, J. Alirezaie

{"title":"Deep Convolutional Approach for Low-Dose CT Image Noise Reduction","authors":"S. Badretale, F. Shaker, P. Babyn, J. Alirezaie","doi":"10.1109/ICBME.2017.8430255","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430255","url":null,"abstract":"An essential objective in medical low-dose Computed Tomography (CT) imaging is how best to preserve the quality of the image. While, reducing the X-ray radiation dose is desired, in general, the image quality lowers by reducing the dose. Therefore, improving image quality is remarkably crucial for diagnostic purposes. A novel method to denoise low-dose CT images has been presented in this study. Different from the prevalent and traditional algorithms which utilize similar shared features of CT images in the spatial or transform domain, the deep learning approach is suggested for low-dose CT denoising. In this paper, a fully convolutional neural network architecture consisting of five parts, namely-Feature extraction, Compressing, Mapping, Enlarging, and Assembling, are introduced to directly map the low-dose CT images onto the corresponding normal-dose CT images. The results of the proposed technique were compared with three state-of-the-art algorithms. To illustrate the superiority of our proposed technique, three performance measures, including root mean squared error, peak signal to noise ratio, and structural similarity index are presented.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758342","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

Automatic Diagnosis of Melanoma Using Log-Linearized Gaussian Mixture Network 基于对数线性化高斯混合网络的黑色素瘤自动诊断

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430224

A. Zakeri, Sina Soukhtesaraie

{"title":"Automatic Diagnosis of Melanoma Using Log-Linearized Gaussian Mixture Network","authors":"A. Zakeri, Sina Soukhtesaraie","doi":"10.1109/ICBME.2017.8430224","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430224","url":null,"abstract":"Melanoma is the most malignant type of pigmented skin lesions whose early diagnosis is the only treatment key. This paper presents a decision support system for automatic melanoma recognition using log-linearized Gaussian mixture neural network (LLGMNN). Here, some image preprocessing steps precede segmentation to remove artifacts. Next Otsu thresholding method is utilized to detect lesion from the surrounding healthy skin. Then related features including shape and border characteristics, color, and texture features are extracted. A mutual information based feature selection technique is used to find the optimal subset of attributes. Here, two different structures of LLGMNN are designed and validated for our pattern classification problem, one for detection of melanoma from non-melanoma lesions and the other one for discrimination between melanoma, dysplastic, and benign lesions. The proposed system is evaluated on a set of 792 dermoscopy images. Classification results show the accuracy of 89.8%, 88.3%, and 91.2 % for melanoma, dysplastic, and benign lesions, respectively. Results show that the proposed system is efficient, and achieve acceptable classification accuracies.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125558940","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

On the Effects of Transcranial Direct Current Stimulation on Hand Movement in Parkinson's Disease: A Primary Study 经颅直流电刺激对帕金森病患者手部运动影响的初步研究

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430217

S. Marzban, Tirdad Seifi Ala, F. Towhidkhah, B. Forogh, Seyed Amirhassan Habibi

{"title":"On the Effects of Transcranial Direct Current Stimulation on Hand Movement in Parkinson's Disease: A Primary Study","authors":"S. Marzban, Tirdad Seifi Ala, F. Towhidkhah, B. Forogh, Seyed Amirhassan Habibi","doi":"10.1109/ICBME.2017.8430217","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430217","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative disorder that mainly affects the motor areas of patients, although cognitive disorders are common among them, too. Various invasive and non-invasive neuromodulations have been proposed for treatment of the symptoms of PD, in which tDCS is one of the emerging methods. In this study, six right-handed patients with PD in drug-off condition were stimulated with 2mA tDCS in left primary motor cortex (Ml) for 20 minutes. Two new tasks were performed by the participants thrice pre and post stimulation to limit the effects of learning or anxiety. The trajectory signals of their hand movements were recorded using a graphical tablet. To investigate the effects of tDCS on the performance of hand movement in the participants, various features were extracted from the signals. Wilcoxon signed-rank test was applied to statistically evaluate them. The results showed that the time, velocity, acceleration and Approximate Entropy (ApE n) of participants improved significantly (p < 0.05) in spiral drawing task. PCA was also performed on difference of velocity signals. Ratio of power in 0.1-5 Hz to 5–10 Hz, peak of frequency, and Lyapunov exponent of first principle component showed meaningful changes (p < 0.05). However, tDCS had no significant effects on the performance of writing a Farsi sentence. The features extracted from the standardized motor tasks provided in this study indicated that tDCS could be capable of improving hand movement in PD.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117082158","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

A Criterion to Evaluate Feature Vectors Based on ANOVA Statistical Analysis 基于方差分析的特征向量评价准则

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430266

Abbas Salami, F. Ghassemi, Mohammad Hasan Moradi

{"title":"A Criterion to Evaluate Feature Vectors Based on ANOVA Statistical Analysis","authors":"Abbas Salami, F. Ghassemi, Mohammad Hasan Moradi","doi":"10.1109/ICBME.2017.8430266","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430266","url":null,"abstract":"The objective of this research is to evaluate feature vectors based on statistical analysis, focusing on application in brain-computer interface (BCI) domain. Common spatial pattern (CSP) is one of the most frequently used algorithms in BCI to extract features from electroencephalogram (EEG). However, since CSP would be a greedy algorithm by solving it through eigenvalue decomposition method, choosing features in a sequential way does not necessarily result in the minimal achievable classification error for higher than 2-dimensional feature vectors. To overcome this issue, Unbalanced Factorial ANOVA (UF-ANOVA) analysis based on linear regression has been used in order to evaluate features extracted from CSP algorithm. Finally, a criterion based on Mahalanobis distance and F distribution parameter resulted from ANOVA table is introduced to evaluate feature vectors. It is shown that proposed criterion is compatible with widely used criterions such as Fisher score (FS) and Mutual information (MI). Moreover, proposed analysis is not limited to one-dimensional feature vectors and can be applied to higher dimensions.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129950271","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

Quantification of Depression Disorder Using EEG Signal 用脑电图信号量化抑郁障碍

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430237

M. Hajian, M. Hassan Moradi

{"title":"Quantification of Depression Disorder Using EEG Signal","authors":"M. Hajian, M. Hassan Moradi","doi":"10.1109/ICBME.2017.8430237","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430237","url":null,"abstract":"Depression is one of the most common psychological disorders, which has been an ever-growing concern in science world. In mental health questionnaires, including Beck's questionnaire, the patient is assigned a numerical indicator. Researches have shown that level of depression in people is associated with structural changes in the brain, therefore, by analyzing brain signals, it is possible to detect depression level. This paper presents a method that estimates the beck's index of each subject by extracting specific features from the patient's EEG signal. In order to quantify depression, an algorithm has been designed and implemented that uses membership values obtained from the fuzzy classifier and the support vector machine(SVM). In this approach, desirable results have been obtained indicating that the proposed algorithm has a good ability to determine the numerical index for depression. The results were obtained with a percent relative difference(PRD) of 5% and a Pearson correlation of 0.92. The results of the experiments show that the estimated numerical value of the designed system is of high correlation and low amount of PRD in comparison with the original beck number, related to each person.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126705626","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

Bilateral Control of a Nonlinear Teleoperation Robotic System with Time Varying Delay Using Optimal Control Method 一类时变时滞非线性遥操作机器人系统的双边最优控制

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430241

Abasali Amini, A. Mirbagheri, Amir Homayoun Jafari

{"title":"Bilateral Control of a Nonlinear Teleoperation Robotic System with Time Varying Delay Using Optimal Control Method","authors":"Abasali Amini, A. Mirbagheri, Amir Homayoun Jafari","doi":"10.1109/ICBME.2017.8430241","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430241","url":null,"abstract":"In this paper, an architecture for bilateral control of force and position in a teleoperation robotic system with nonlinear model of 1 DOF has been introduced. We assumed a time varying delay of up to 150 millisecond in both forward (master to slave) and backward (slave to master) communication channels. At the 1st stage two separate PID controller with specially tuned parameters are used for each master and slave robot which locally control each robots in an impedance bilateral control architecture of force at master side and position at slave side. At the 2nd stage an optimal controller is used for each side with the same architecture of impedance bilateral control method. Then the position tracking of slave side commanded from master one and force tracking of master side which commanded from slave one has been investigated for both proposed methods. Although the PID controller was performed acceptable in position control of the slave robot, but at the master side the robot oscillates when the slave robot touch the remote environment. However the optimal controller at both master and slave sides, established a stable position track and transparent force feedback.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670117","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 Clustering-Based Algorithm for De Novo Motif Discovery in DNA Sequences 基于聚类的DNA序列De Novo Motif发现算法

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430242

Mohammad Haghir Ebrahim-Abadi, E. Fatemizadeh

{"title":"A Clustering-Based Algorithm for De Novo Motif Discovery in DNA Sequences","authors":"Mohammad Haghir Ebrahim-Abadi, E. Fatemizadeh","doi":"10.1109/ICBME.2017.8430242","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430242","url":null,"abstract":"Motif discovery is a challenging problem in molecular biology and has been attracting researcher's attention for years. Different kind of data and computational methods have been used to unravel this problem, but there is still room for improvement. In this study, our goal was to develop a method with the ability to identify all the TFBS signals, including known and unknown, inside the input set of sequences. We developed a clustering method specialized as part of our algorithm which outperforms other existing clustering methods such as DNACLUST and CD-HIT-EST in clustering short sequences. A scoring system was needed to determine how much a cluster is close to being a real motif. Multiple features are calculated based on the contents of each cluster to determine the score of the cluster. These features contain a set of divergence measures, positional, and occurrence information. These scores are combined in a way that a trade-off between them determines the clusters situation. There is an option to compare the final results with the motif databases such as Jolma2013, and UniProbe using Tomtom motif comparison tool. Algorithm Evaluation has been performed on three datasets from ABS database.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131655530","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

Recognition of Positive, Negative and Neutral Emotions Using Brain Connectivity Patterns 利用大脑连接模式识别积极、消极和中性情绪

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430281

Javad Nematollahi, M. Firoozabadi

{"title":"Recognition of Positive, Negative and Neutral Emotions Using Brain Connectivity Patterns","authors":"Javad Nematollahi, M. Firoozabadi","doi":"10.1109/ICBME.2017.8430281","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430281","url":null,"abstract":"There are various resources inside the brain. Brain activities are the result of these sources or the result of their connectivity. Therefore, any special emotion should also be the result of various connectivity chains among the brain's resources. Studying this connectivity chains could help us recognize the corresponding emotions. The aim of this paper is to find interaction patterns in positive, neutral and negative emotions, and to recognize different types of emotions. We have used DEAP data in this project. These datasets were gathered from 32 volunteers, half of whom were women. Playing different types of music, caused them to experience special emotions, and their brain signals were recorded simultaneously. Music videos belonged to three different classes: positive, neutral and negative. After preprocessing the signals, we have achieved the connectional characteristics among the various channels, including causal features in various delays. Utilizing Davis-Bouldin Method, we obtained the sub-group of the optimal features. To evaluate the obtained results, we used SVM and KNN clustering methods. The final classified results, describes more favorable performance of interactional patterns and show the fact that connectional features can classify the classes in two arousal and valence with accuracy %79.7 and %88.2 respectively, which had %6 and %12.54 increase with respect to other traditional features.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128846026","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

Investigation of the Effect of Adding Stem Cells-as an Therapeutic Suggestion-on the Immune System's Response to the Cancerous Cells: A Mathematical Approach 添加干细胞作为治疗建议对免疫系统对癌细胞反应的影响的研究:一种数学方法

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430249

Z. Veisi, S. Ravanshadi, Heydar Khadem, Heydar Khadem

{"title":"Investigation of the Effect of Adding Stem Cells-as an Therapeutic Suggestion-on the Immune System's Response to the Cancerous Cells: A Mathematical Approach","authors":"Z. Veisi, S. Ravanshadi, Heydar Khadem, Heydar Khadem","doi":"10.1109/ICBME.2017.8430249","DOIUrl":"https://doi.org/10.1109/ICBME.2017.8430249","url":null,"abstract":"Cancer has been triggered by mutations in normal cells, and these mutations react to the immune system. Investigating the behavior of cells in this situation is very challenging based on empirical analysis and can sometimes not be performed. However, mathematical tools can make it easy to study this subject. In this work, we investigated the immune system response using differential equations in two steps: first, rapid attack of NK cells; second, complementary reaction of macrophages and T-cells. For the cases which mutant cells escape from the immune system attack, it is suggested that immunotherapy be performed by adding stem cells to help the immune system. In this study, the effectiveness of the defense against mutation cells is modeled in different conditions: first immune response, barely; first and second immune response together; and finally, first and second immune response along with injected stem cells as a therapeutic proposition. To show the effect of stem cells on making defense more efficient, we have studied stability of the system in divergent situations and compared equilibrium points of the model in these situations.","PeriodicalId":116204,"journal":{"name":"2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126232051","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

Voltage Transfer Functions for in-Vitro Cell Stimulation: A Computational Study 体外细胞刺激的电压传递函数:计算研究

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2017-11-01 DOI: 10.1109/ICBME.2017.8430245

Amir Mohammad Shamaee, M. Saviz

引用次数: 0