2010 17th Iranian Conference of Biomedical Engineering (ICBME)最新文献_第2页

QRS detection based on matching pursuit algorithm 基于匹配追踪算法的QRS检测

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704914

S. Shamekhi, M. Sedaaghi

引用次数: 6

Lesion detection in dermoscopy images using Sarsa Reinforcement algorithm 基于Sarsa强化算法的皮肤镜图像病变检测

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704964

S.Mohammad Seyyed Ebrahimi, H. Pourghassem, M. Ashourian

{"title":"Lesion detection in dermoscopy images using Sarsa Reinforcement algorithm","authors":"S.Mohammad Seyyed Ebrahimi, H. Pourghassem, M. Ashourian","doi":"10.1109/ICBME.2010.5704964","DOIUrl":"https://doi.org/10.1109/ICBME.2010.5704964","url":null,"abstract":"Dermoscopy is one of the major imaging techniques used in diagnoses of Melanoma and other skin diseases. Because of difficulties and subjectivity of human interpretation, automatic and computerized analysis of dermoscopic images has opened an important research area. Skin lesion detection is as the first step in this analysis. Finding an optimal threshold for segmenting the lesion is a severe task in image processing. Different methods for thresholding already exist. In this work, we use a combination of well-known thresholding methods and fuse them by Sarsa Reinforcement algorithm which leads to a reinforced threshold. The reinforced agent learns optimal weights for different thresholding methods and finally segments the dermoscopic image with optimal threshold. A reward function is designed for achieving the similarity ratio between the binary output image and original gray level image and calculating reward/punish signal which should be exerted to reinforced agent. We use three thresholding methods for combination in the reinforced agent and the detected lesions are compared with the ground-truth which is determined by three different dermatologists.","PeriodicalId":377764,"journal":{"name":"2010 17th Iranian Conference of Biomedical Engineering (ICBME)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129514540","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: 10

Automatic fontanel extraction from newborns' CT-images using a model based level set method 基于模型水平集的新生儿ct图像囟门自动提取方法

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704949

N. Jafarian, K. Kazemi, R. Grebe, M. Helfroush, M. Dehghani, H. Abrishami-Moghaddam, Catherine Gondary-Jouet, F. Wallois

{"title":"Automatic fontanel extraction from newborns' CT-images using a model based level set method","authors":"N. Jafarian, K. Kazemi, R. Grebe, M. Helfroush, M. Dehghani, H. Abrishami-Moghaddam, Catherine Gondary-Jouet, F. Wallois","doi":"10.1109/ICBME.2010.5704949","DOIUrl":"https://doi.org/10.1109/ICBME.2010.5704949","url":null,"abstract":"The newborn's skull is composed of already ossified parts of the flat bone connected by areas of fibrous membrane not yet ossified, which are called fontanels. At birth, an infant has six of such fontanels. These two different tissue types forming the outer part of the neuro-cranium have different electrical conductivities. Thus, it is important to determine the exact geometry of the fontanels if one aims to solve the inverse problem as e.g. for source localization. Computer Tomography (CT) imaging provides an excellent tool for the non-invasive study of bone which here can easily be identified due to its high contrast as compared to other tissue. Fontanels correspond to not yet ossified cartilage and give less contrast, thus they can be indirectly reconstructed by extrapolation for closing of the gaps between the flat bones forming the skull. In this paper, we propose an automatic model based method using level set to extract the fontanels from CT images. The automatically determined fontanels show good agreement with the manually extracted ones.","PeriodicalId":377764,"journal":{"name":"2010 17th Iranian Conference of Biomedical Engineering (ICBME)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129625671","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: 6

Chaotic features analysis of EEG signals during standard tasks of Waterloo-Stanford 滑铁卢-斯坦福标准任务中脑电信号的混沌特征分析

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704924

Elahé Yargholi, A. Nasrabadi

引用次数: 0

Landmark detection on cephalometric radiology images through combining classifiers 结合分类器的头颅影像学图像地标检测

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704950

M. Farshbaf, A. Pouyan

引用次数: 3

A comparative study of feature extraction methods in P300 detection P300检测中特征提取方法的比较研究

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704928

Zahra Amini, V. Abootalebi, M. Sadeghi

引用次数: 7

Content based mammogram image retrieval based on the multiclass visual problem 基于多类视觉问题的基于内容的乳房x线图像检索

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704958

F. Siyahjani, E. Fatemizadeh

引用次数: 1

Bioinformatics analysis of schwanniomyces occidentalis alpha amylase secretion signal sequences 西雪旺菌α淀粉酶分泌信号序列的生物信息学分析

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5705025

S. Ziaei, I. Nahvi, M. Mobini-Dehkordi, M. Tavassoli

{"title":"Bioinformatics analysis of schwanniomyces occidentalis alpha amylase secretion signal sequences","authors":"S. Ziaei, I. Nahvi, M. Mobini-Dehkordi, M. Tavassoli","doi":"10.1109/ICBME.2010.5705025","DOIUrl":"https://doi.org/10.1109/ICBME.2010.5705025","url":null,"abstract":"The amy gene from schwaniomyces occidentalis which encodes a secretory alpha amylase isolated by primers that designed from sequences derived from Gene bank. The isolated gene is then cloned into a p426gpd vector. After cloning, the recombinant vector was transformed into E.Coli DH5x strain and recombinant vector was sent for sequencing by T7, T3 and a mid primer. After sequencing, the sequence of the alpha amylase gene is then converted to amino acid sequence and the new hypothetical protein was analyzed by several signal peptide analysis programs. The cleavage sites and intracellular localization of the protein inside the cell is detected by this analysis. The results showed that schwanniomyces occidentalis alpha amylase has a 25 amino acid long secretory signal peptide that is responsible for initiation of transportation of the enzyme across endoplasmic reticulum, through the secretory pathway and finally secretion out of the cell. Our result would prove very important not only for production of these enzymes but also protein analysis and creating the recombinant organism that produce these enzymes.","PeriodicalId":377764,"journal":{"name":"2010 17th Iranian Conference of Biomedical Engineering (ICBME)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131624195","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

Brain tissue segmentation using an unsupervised clustering technique based on PSO algorithm 基于粒子群算法的无监督聚类脑组织分割

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704938

M. Azarbad, A. Ebrahimzadeh, A. Babajani-Feremi

引用次数: 15

Classification of ADHD/normal participants using frequency features of ERP's Independent Components 利用ERP独立分量的频率特征对ADHD/正常参与者进行分类

2010 17th Iranian Conference of Biomedical Engineering (ICBME) Pub Date : 2010-11-01 DOI: 10.1109/ICBME.2010.5704916

F. Ghassemi, M. Moradi, M. Tehrani‐Doost, V. Abootalebi

{"title":"Classification of ADHD/normal participants using frequency features of ERP's Independent Components","authors":"F. Ghassemi, M. Moradi, M. Tehrani‐Doost, V. Abootalebi","doi":"10.1109/ICBME.2010.5704916","DOIUrl":"https://doi.org/10.1109/ICBME.2010.5704916","url":null,"abstract":"This study investigates the Event Related Potentials (ERP) obtained from Independent Components of EEG (ERPIC) while participants performed a sustained attention task. EEG signals were recorded from 50 adult participants including ADHD and normal subjects while performing Continuous Performance Test (CPT). Signals were recorded from 21 Ag/AgCl electrodes according to the international 10–20 standard. Independent Component Analysis (ICA) was used as the processing method. For ERP extraction, average of each group of signals which were time-locked to the onset of stimuli was calculated. Several frequency features were extracted from different ERPICs. High accuracy (92%) was achieved in classification of clinical and non-clinical participants using combination of two features in a K-Nearest Neighbors (KNN) classifier. Nine pairs of features resulted in such accuracy, while most of the best features are related to the power in γ band which is consistent with the previous studies. Regarding the ERP groups, most of the best features are related to wrong answered targets and to time block ERPICs. The results revealed a promising relation between clinical situation of the participants and some parameters of brain independent components which can be used for further evaluations of the sustained attention level.","PeriodicalId":377764,"journal":{"name":"2010 17th Iranian Conference of Biomedical Engineering (ICBME)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131252275","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: 7