2014 21th Iranian Conference on Biomedical Engineering (ICBME)最新文献

A time-delay parallel cascade identification system for predicting jaw movements 一种预测下颌运动的时滞平行级联识别系统

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-26 DOI: 10.1109/ICBME.2014.7043936

Nazanin Goharian, Hadi Kalani, Sanar Moghimi

引用次数: 2

Ice-templated scaffolds of bioglass nanoparticles reinforced-chitosan 冰模板生物玻璃纳米颗粒增强壳聚糖支架

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043892

Masoud Pourhaghgouy, A. Zamanian

引用次数: 2

Biological constrained learning of parameters in a recurrent neural network-based model of the primary visual cortex 基于递归神经网络的初级视觉皮层模型中参数的生物约束学习

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043938

E. Lotfi, Babak Nadjar Araabi, M. N. Ahmadabadi, L. Schwabe

引用次数: 1

Numerical and experimental estimating zona pellucida hardness under microinjection to assess oocyte quality 用数值和实验方法估计显微注射下透明带硬度，评价卵母细胞质量

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043901

M. Khalilian, M. R. Valojerdi, A. Rouhollahi

{"title":"Numerical and experimental estimating zona pellucida hardness under microinjection to assess oocyte quality","authors":"M. Khalilian, M. R. Valojerdi, A. Rouhollahi","doi":"10.1109/ICBME.2014.7043901","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043901","url":null,"abstract":"The zona pellucida (ZP) is the extracellular coat that surrounds mammalian oocytes. The precise determination of ZP hardness is mainly unknown due to the lack of appropriate measuring systems and modelling methods. In this study, we have used experimental and numerical models to explain the mechanical behavior of a single oocyte cell to improve the assisted reproductive technology (ART) outcomes by assessing oocyte/embryo quality. This paper presents the development of a microinjection model to estimate the ZP hardness and an experimental procedure to obtain the required data for this model. Our results show that the estimated penetration force provides a performance target for the penetration process during intracytoplasmic sperm injection (ICSI), while the estimated corresponding hardness serves as an indicator of the amount of deformation experienced by the oocyte before penetration. Evaluation of these results shows that a routine assessment of ZP hardness under microinjection would allow for the identification of certain oocyte pools for which further manipulation is recommended in order to increase injection, hatching and finally ART outcomes.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121066801","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

Atlas-based automatic breast MRI segmentation using pectoral muscle and chest region model 基于阿特拉斯的胸肌胸区自动分割

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043932

Aida Fooladivanda, S. B. Shokouhi, M. Mosavi, N. Ahmadinejad

{"title":"Atlas-based automatic breast MRI segmentation using pectoral muscle and chest region model","authors":"Aida Fooladivanda, S. B. Shokouhi, M. Mosavi, N. Ahmadinejad","doi":"10.1109/ICBME.2014.7043932","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043932","url":null,"abstract":"Accurate breast MRI segmentation is an important processing step in Computer Aided Diagnosis (CAD) systems and breast density assessment. Most of the atlas-based breast segmentation methods employ breast area as the template. Instead, we use both pectoral muscle and chest region model as the template, because there is great variability in breast shape and signal intensity. Pectoral muscle and chest region place in similar locations with similar shape and signal intensity. We demonstrate the high quality of the defined template for our atlas-based system. The presented approach is validated with a dataset of 2800 bilateral axial breast MR images from 50 women that include all of Breast Imaging Reporting and Data System (BI-RADS) breast density range. Five quantitative metrics as Dice Similarity Coefficient (DSC), Jaccard Coefficient (JC), total overlap, False Negative (FN) and False Positive (FP) are computed to compare similarity between automatic and manual segmentations. Our proposed algorithm obtains DSC, JC, total overlap, FN and FP values of 0.85, 0.75, 0.83, 0.16 and 0.11, respectively.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123244255","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: 9

A multi-scale cell-based model of lumen formation in single endothelial cell 单个内皮细胞内腔形成的多尺度细胞模型

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043897

H. Bazmara, M. Sefidgar, M. Bazargan, M. Musavi, M. Soltani

引用次数: 0

A mathematical model for tremor genesis in Parkinson disease from a chaotic view 从混沌的观点看帕金森病震颤发生的数学模型

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043950

Fatemeh Ghoreishian, M. Pooyan

引用次数: 1

Fuzzy c-means clustering method based on prior knowledge for brain MR image segmentation 基于先验知识的模糊c均值聚类方法在脑磁共振图像分割中的应用

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043928

M. Yazdi, Mohammad Khalilzadeh, M. Foroughipour

{"title":"Fuzzy c-means clustering method based on prior knowledge for brain MR image segmentation","authors":"M. Yazdi, Mohammad Khalilzadeh, M. Foroughipour","doi":"10.1109/ICBME.2014.7043928","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043928","url":null,"abstract":"Image segmentation is mostly used as a fundamental step in medical image processing, especially for clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) algorithm is one of the well known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MR images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentation done by experts, prior knowledge can be useful to modify image segmentation methods. In this article we proposed a method to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in order to modify segmentation of brain MR images with high noise level and spatial complexities. In real images, we had considerable improvement in similarity index of three classes (white matter, gray matter, cerebrospinal fluid) and in simulated images with different noise levels evaluation criteria of white matter and gray matter improved.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121187854","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

K-complex identification in sleep EEG using MELM-GRBF classifier 基于MELM-GRBF分类器的睡眠脑电k复合体识别

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043905

Seyed Mohammad Reza Noori, Amin Hekmatmanesh, M. Mikaeili, K. Sadeghniiat-haghighi

引用次数: 21

Investigation of Brain Default Network's activation in autism spectrum disorders using Group Independent Component Analysis 孤独症谱系障碍脑默认网络激活的群体独立成分分析研究

2014 21th Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2014-11-01 DOI: 10.1109/ICBME.2014.7043916

Arezoo Alizadeh, E. Fatemizadeh, M. Deevband

{"title":"Investigation of Brain Default Network's activation in autism spectrum disorders using Group Independent Component Analysis","authors":"Arezoo Alizadeh, E. Fatemizadeh, M. Deevband","doi":"10.1109/ICBME.2014.7043916","DOIUrl":"https://doi.org/10.1109/ICBME.2014.7043916","url":null,"abstract":"Autism Spectrum Disorders (ADS), with unknown etiology, is one of the most understudy fields of research worldwide that requires complicated and delicate analytical study methods. The purpose of this study was to compare active regions of Brain Default Mode Network (DMN) using Group Independent Component Analysis (6ICA) among resting state patients with Autism Disorder and healthy subjects. Default Mode Network consists of posterior cingulate cortex (PCC), lateral parietal cortex/angular gyrus retrosplenial cortex, medial prefrontal cortex, superior frontal gyrus, parahippocampal gyrus and temporal lobe shows more prominent activity in passive resting conditions. The diagnosis of autism disorder was According to the clinical ADI-R of ADOS test. After Data preprocessing of rs-fMRI dataset by SPM toolbox, Group independent component analysis was performed in three steps of data reduction, ICA, and back reconstruction using the group ICA of fMRI toolbox (GIFT). Group ICA revealed sixteen Default mode network components which five of them were chosen as DMN components to compare between both groups. The number of voxels in each cluster of autistic individuals was significantly lower than in healthy individuals. Spatial group ICA of fMRI can be a useful approach to determine and study of differences in DMN of brain in patients with autism disorder.","PeriodicalId":434822,"journal":{"name":"2014 21th Iranian Conference on Biomedical Engineering (ICBME)","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114819583","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