2016 1st International Conference on Biomedical Engineering (IBIOMED)最新文献

Comparative study on data mining classification methods for cervical cancer prediction using pap smear results 巴氏涂片结果预测宫颈癌数据挖掘分类方法的比较研究

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869827

Y. Kurniawati, A. E. Permanasari, S. Fauziati

引用次数: 22

Feature extraction for palmprint recognition using kernel-PCA with modification in Gabor parameters 基于Gabor参数修改的核主成分分析掌纹识别特征提取

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869820

M. Kusban, A. Susanto, O. Wahyunggoro

引用次数: 10

Segmentation of malaria parasite candidates from thick blood smear microphotographs image using active contour without edge 利用无边缘活动轮廓分割厚血涂片显微照片中的候选疟原虫

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869824

Sekar Rini Abidin, U. Salamah, A. Nugroho

{"title":"Segmentation of malaria parasite candidates from thick blood smear microphotographs image using active contour without edge","authors":"Sekar Rini Abidin, U. Salamah, A. Nugroho","doi":"10.1109/IBIOMED.2016.7869824","DOIUrl":"https://doi.org/10.1109/IBIOMED.2016.7869824","url":null,"abstract":"Malaria is a serious health problem in Indonesia caused by malaria parasites. Early detection of Malaria is an important step to an effective treatment. Malaria parasite identification should be carried out based on observation on at least 100 fields view strong magnification of thick blood smears. Malaria parasite detection process is usually carried out with a microscope observation. But it consumes too much time and the number experts are limited. To overcome these obstacles, we developed a computer aided diagnosis system to automatically detecting malaria parasites. Parasite image segmentation is an important step in the detection process. But segmentation of malaria parasite that consists of a nucleus and cytoplasm in a thick blood smear is not easy because the boundary between object and background is not clear and has a low contrast. This study proposed a solution to the problem of segmentation of malaria candidate parasite candidates from thick blood smears. The proposed method focused on image enhancement and segmentation steps. Image enhancement consists of lowpass filtering to reduce noise and contrast stretching to increase contrast. Segmentation is used to detect object using active contour without edge, then erosion, dilation, masking, contrast stretching, and thresholding. The result showed that the proposed method is capable to segment malaria parasite candidates from thick blood smear with 97.57% accuracy, 12.04% (283 pixels) false negative rate (FNR), and 6.87% (202 pixels) false discovery rate (FDR), from 19600 pixels total in each image.","PeriodicalId":171132,"journal":{"name":"2016 1st International Conference on Biomedical Engineering (IBIOMED)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127106553","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

Emergency alert prediction for elderly based on supervised learning 基于监督学习的老年人紧急警报预测

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869816

Kurnianingsih, L. Nugroho, Widyawan, Lutfan Lazuardi, A. S. Prabuwono

{"title":"Emergency alert prediction for elderly based on supervised learning","authors":"Kurnianingsih, L. Nugroho, Widyawan, Lutfan Lazuardi, A. S. Prabuwono","doi":"10.1109/IBIOMED.2016.7869816","DOIUrl":"https://doi.org/10.1109/IBIOMED.2016.7869816","url":null,"abstract":"At the older age, the likelihood of disability increases and hence the increasing need for long-term care and facilities to assist elderly people who endure gradual loss of body function. Early detection of changes in health condition of elderly can increase safety for elderly people in emergency conditions. Alert prediction can be viewed as an assistive technology that will deliver appropriate escalation in the earliest time so that elderly can receive immediate responses. Supervised learning can be used as a tool to predict alert in emergency condition by training historical data of elderly behaviors and conditions. This paper proposed emergency alert prediction using supervised learning algorithms. Three algorithms of supervised learning, namely deep learning, k-NN, and LVQ were used to simulate the proposed system. The objective of this paper is to investigate the performance of three algorithms in making emergency alert prediction for elderly living independently. We conducted experiments for 30 days to elderly living independently and we obtained 1038 datasets. The simulation results showed deep learning performed the best accuracy 99.57% correct. Whereas k-NN obtained the best accuracy 90.79% correct, and LVQ obtained the best accuracy 80.32%.","PeriodicalId":171132,"journal":{"name":"2016 1st International Conference on Biomedical Engineering (IBIOMED)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121934522","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

Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification 基于tsallis熵的肺声特征提取

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869823

Achmad Rizal, Risanuri Hidayat, H. A. Nugroho

引用次数: 16

Comparison of pre- and post-reconstruction denoising approaches in positron emission tomography 正电子发射断层成像重建前后去噪方法的比较

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869821

Sicong Yu, H. Muhammed

引用次数: 3

Management information systems development for veterinary hospital patient registration using first in first out algorithm 采用先进先出算法的兽医院病人登记管理信息系统开发

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869829

Dian Hapsari, A. E. Permanasari, S. Fauziati, Ida Fitriana

引用次数: 5

Comparison of radialis sphygmomanometer in evaluating the blood pressure of healthy volunteers radialis血压计在健康志愿者血压评估中的比较

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869817

H. Rachmat

{"title":"Comparison of radialis sphygmomanometer in evaluating the blood pressure of healthy volunteers","authors":"H. Rachmat","doi":"10.1109/IBIOMED.2016.7869817","DOIUrl":"https://doi.org/10.1109/IBIOMED.2016.7869817","url":null,"abstract":"In this study, we compared the blood pressure measurement that obtained by a radialis sphygmomanometer with two others non-invasive sphygmomanometer types i.e. an aneroid sphygmomanometer and a brachial sphygmomanometer. The aim of this study was to evaluate statistically the blood pressure values of a radialis sphygmomanometer relative two other types of sphygmomanometer. This study was performed as a validation test before modifying the existing display-based radialis sphygmomanometer to a voice-based radialis sphygmomanometer. By developing the radialis sphygmomanometer with voice output, we believed that a blind people can be possible to measure the blood pressure easily, independently and periodically. To collect the data measurements, an experience nurse measured two blood pressure values i.e. systolic and diastolic of 30 healthy adult volunteers (17 female, 13 male; mean age 40.87±13.97 years, range: 16-75 years) with three diffent types of sphygmomanometer. A mercury sphygmomanometer was used at first and continued by a brachial sphygmomanometer and a radialis sphygmomanometer in order. Data were then analyzed statically using one way ANOVA in SPSS 18. The statistic results showed that the average measurement values were equal among three types of sphygmomanometer with significance (α) of systole and diastole bigger than 0.05 i.e. 0.055 and 0.178, respectively.","PeriodicalId":171132,"journal":{"name":"2016 1st International Conference on Biomedical Engineering (IBIOMED)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125555290","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

Classification of breast ultrasound images based on texture analysis 基于纹理分析的乳腺超声图像分类

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869825

M. Rahmawaty, H. A. Nugroho, Yuli Triyani, I. Ardiyanto, I. Soesanti

{"title":"Classification of breast ultrasound images based on texture analysis","authors":"M. Rahmawaty, H. A. Nugroho, Yuli Triyani, I. Ardiyanto, I. Soesanti","doi":"10.1109/IBIOMED.2016.7869825","DOIUrl":"https://doi.org/10.1109/IBIOMED.2016.7869825","url":null,"abstract":"Ultrasonography (USG) is a popular imaging modality because of its flexibility, non-invasion, non-ionisation and low cost. A breast ultrasound used to detect and classify abnormalities of the breast mass. However, the diagnosis is very subjective because it depends on the ability of the radiologist. In order to eliminate operator dependency and to improve the diagnostic accuracy, a computerised system is necessary to do the feature extraction and the classification of the breast nodule. This research proposes a classification of breast USG images by using some texture features into two classes. The dataset consists of 57 USG images which grouped into 27 anechoic cases and 30 hypoechoic cases. An initial step of image pre-processing is conducted to enhance the detection capability. Afterwards, followed by some methods of morphological operation, region growing active contour and histogram equalization. The feature extraction method used texture analysis, which is histogram, gray level co-occurrence matrix (GLCM) and fractal Brownian motion (FBM). Finally, Multilayer Perceptron (MLP) classification method is used to classify anechoic nodule from hypoechoic nodule. The result shows that the proposed method achieved the accuracy of 91.23%, sensitivity of 95.83%, specificity of 87.88%, Positive Predictive Value (PPV) of 85.19% and Negative Predictive Value (NPV) of 96.67%. This suggest that the proposed method is excellent in analyzing breast USG images.","PeriodicalId":171132,"journal":{"name":"2016 1st International Conference on Biomedical Engineering (IBIOMED)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130709460","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: 17

A prototype of SSVEP-based BCI for home appliances control 基于ssvep的家用电器控制BCI原型

2016 1st International Conference on Biomedical Engineering (IBIOMED) Pub Date : 2016-10-01 DOI: 10.1109/IBIOMED.2016.7869810

Sinantya Feranti Anindya, H. Rachmat, E. Sutjiredjeki

引用次数: 27