Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing最新文献

Micro Disposable Biometric Authentication: An Application Using Fingernail Minute Textures for Nonsensitive Services 微型一次性生物识别认证:一种使用指甲微小纹理进行非敏感服务的应用

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288209

Genki Sugimoto, Masahiro Fujita, Yuto Mano, Tetsushi Ohki, M. Nishigaki

引用次数: 1

Comparing the Performances of PCA (Principle Component Analysis) and LDA (Linear Discriminant Analysis) Transformations on PAF (Paroxysmal Atrial Fibrillation) Patient Detection 主成分分析(PCA)与线性判别分析(LDA)变换在阵发性心房颤动(PAF)诊断中的性能比较

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288201

Safa Sadaghiyanfam, M. Kuntalp

引用次数: 8

Tissue Region Growing for Hispathology Image Segmentation 组织区域生长在病理图像分割中的应用

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288213

Zhaoyang Xu, C. F. Moro, D. Kuznyecov, B. Bozóky, Le Dong, Qianni Zhang

{"title":"Tissue Region Growing for Hispathology Image Segmentation","authors":"Zhaoyang Xu, C. F. Moro, D. Kuznyecov, B. Bozóky, Le Dong, Qianni Zhang","doi":"10.1145/3288200.3288213","DOIUrl":"https://doi.org/10.1145/3288200.3288213","url":null,"abstract":"The accurate identification of the tumour tissue border is of crucial importance for histopathology image analysis. However, due to the high morphology variance in histology images, especially in border regions where cancer tissue interfere into the normal region, it is challenging even for the pathologists to define the border, not to say for the machine. In this paper, we present an innovative framework to semantically segment the tumour border area in colorectal liver metastasis (CRLM) on pixel level by integrating the features from deep convolutional networks with spatial and statistical information of the cells. With annotations from the pathologists, a two-level deep neural network including a cell-level model and a tissue-level model, is trained to classify patches from the whole slide scan image. Based on the prediction of trained models, a growing-style algorithm is proposed to finalize the segmentation by leveraging the statistical and spatial properties of the cells. Evaluated against the ground truth created by the experts, the framework demonstrates a significant improvement over a conventional deep network model on the cell-level model or the tissue model alone.","PeriodicalId":152443,"journal":{"name":"Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115105026","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

3D Detection of Spheric and Aspheric Femoral Heads in Coronal MR Images of Patients with Legg-Calve-Perthes Disease Using the Spherical Hough Transform 球面霍夫变换在legg - calf - perthes病患者冠状面MR图像中三维检测球形和非球面股骨头

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288205

Abbas Memiş, S. Albayrak, F. Bilgili

{"title":"3D Detection of Spheric and Aspheric Femoral Heads in Coronal MR Images of Patients with Legg-Calve-Perthes Disease Using the Spherical Hough Transform","authors":"Abbas Memiş, S. Albayrak, F. Bilgili","doi":"10.1145/3288200.3288205","DOIUrl":"https://doi.org/10.1145/3288200.3288205","url":null,"abstract":"This paper presents a study of 3D femoral head detection in coronal magnetic resonance (MR) imaging sequences to detect the spheric and aspheric femoral heads using the Spherical Hough Transform (SHT). Shape structure and functionality of the femoral head can be affected by a wide range of hip disorders. In most of the computerized analysis and assessment cases of the hip joint, segmentation of the proximal femur and femoral head is performed on the image sequences that are provided by the several medical imaging modalities. Determination of initial points, detection of the tissue boundaries and segmentation of the tissues are important challenges in automatic or semi-automatic proximal femur segmentation methods. In this paper, we aimed to perform a preliminary study based on 3D femoral head detection in bilateral MR hip images in coronal plane of patients with Legg-Calve-Perthes disease (LCPD). Firstly, MR sections are divided vertically into two equal halves automatically to separate the healthy and pathological hip joints and then Canny's edge detection method is performed to obtain edge images. Finally, 3D SHT is performed to detect the spherical structure of femoral heads. Automatic detection of the femoral head with SHT provides a starting point for the segmentation of the femoral head ball and proximal femur. Furthermore, it also allows for 3D segmentation of the femoral head sphere with the radius information that it provides with the 3D sphere center coordinates. Performance evaluations and experimental results observed on 8 bilateral MR imaging sequences belong to 6 subjects show that proposed method has promising results.","PeriodicalId":152443,"journal":{"name":"Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129458670","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

Effects of Different Nail Polish Colors on Arterial Blood Oxygen Saturation Values (SpO2) in Pakistani Population 不同指甲油颜色对巴基斯坦人群动脉血氧饱和度(SpO2)的影响

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288202

A. Haq, Kainat Zafar, Mashal Fatima, M. Shafique

{"title":"Effects of Different Nail Polish Colors on Arterial Blood Oxygen Saturation Values (SpO2) in Pakistani Population","authors":"A. Haq, Kainat Zafar, Mashal Fatima, M. Shafique","doi":"10.1145/3288200.3288202","DOIUrl":"https://doi.org/10.1145/3288200.3288202","url":null,"abstract":"Pulse oximetry measures arterial oxygen saturation level (SpO2) in the blood. Low SpO2 value are associated with conditions such as hypothermia, hypoxemia, vascular dysfunction and other respiratory disorders. Although, it has been reported earlier that nail polish effects SpO2 values and gives inaccurate readings, most of these experiments were conducted in Europe and the U.S. Since accuracy of pulse oximeter is affected by the skin color, such results need to be verified in other regions of the world due to the variety in skin pigments amongst different population. The objective of this study was to determine the extent of effect which different colors of nail polish causes on SpO2 values in Pakistani population. Data was acquired from sixty four (64) healthy female volunteers (age=22+4 years). Six different nail polish colors black, blue, red, purple, yellow and green were used. The Masimo (SET) pulse oximeter was used to obtain SpO2 readings. Significant difference was found between nail polished fingers as compared to finger nails without nail polish. The variation in mean values is observed more on dark colors (<9%). Medium colors gave slight differences (<6%). Light colors gave no significant difference (<3%). Results from ANOVA test showed significant difference between the fingers SpO2 values of non-polished versus all polished nail fingers.","PeriodicalId":152443,"journal":{"name":"Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115217765","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

Lung Segmentation Using a Fully Convolutional Neural Network with Weekly Supervision 基于周监督的全卷积神经网络肺分割

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288212

Yuan Huang, F. Zhou

引用次数: 2

ECG Monitoring and Anomaly Detection Based on Compressed Measurements 基于压缩测量的心电监测与异常检测

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288206

Alessandra Galli, C. Narduzzi, G. Giorgi

引用次数: 5

Identification of Multi-Channel Simulated Auditory Event-Related Potentials using a Combination of Principal Component Analysis and Kalman Filtering 结合主成分分析和卡尔曼滤波的多通道模拟听觉事件相关电位识别

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288211

K. Paulson, O. Alfahad

{"title":"Identification of Multi-Channel Simulated Auditory Event-Related Potentials using a Combination of Principal Component Analysis and Kalman Filtering","authors":"K. Paulson, O. Alfahad","doi":"10.1145/3288200.3288211","DOIUrl":"https://doi.org/10.1145/3288200.3288211","url":null,"abstract":"The aim of this paper to develop a new method for separating auditory event-related potentials (ERP) signal from artefacts or noise. In experimental conditions, ERPs can be approximated by weighted sums of Principal Component Analysis (PCA) basis signals calculated from clean data. Projection of measured signals onto the PCA subspace significantly decreases noise. Furthermore, Kalman filtering has been used to optimize the combining of the PCA filtered signal with an a priori expected ERP. The main strength of the proposed algorithm arises from manipulating a priori cross-channel information in the form of a PCA weight covariance matrix. Here, the implementation of the method has been quantified using synthetic multi-channel ERP signals to which known amounts of synthetic noise is added to all the channels. The use of synthetic data means and signal and noise are known and so signal-to-noise enhancement may be quantified. For a wide range of initial SNRs, PCA filtering increases SNR by 10 dB and Kalman filtering yields an additional 10 dB improvement.","PeriodicalId":152443,"journal":{"name":"Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131468201","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

Non-Informative Frame Classification in Colonoscopy Videos Using CNNs 基于cnn的结肠镜视频非信息帧分类

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288207

Rezbaul Islam, Ali Alammari, Jung-Hwan Oh, Wallapak Tavanapong, J. Wong, P. C. Groen

{"title":"Non-Informative Frame Classification in Colonoscopy Videos Using CNNs","authors":"Rezbaul Islam, Ali Alammari, Jung-Hwan Oh, Wallapak Tavanapong, J. Wong, P. C. Groen","doi":"10.1145/3288200.3288207","DOIUrl":"https://doi.org/10.1145/3288200.3288207","url":null,"abstract":"In the US, colorectal cancer is the second leading cause of cancer-related deaths behind lung cancer, causing about 49,000 annual deaths. Colonoscopy is currently the gold standard procedure for colorectal cancer screening. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have been investigating an 'automated feedback system' which measures quality of colonoscopy automatically by analyzing colonoscopy video frames in order to assist the endoscopist to improve the quality of the actual procedure being performed. One of the fundamental steps analyzing colonoscopy video frames for the automated quality feedback system is to distinguish non-informative frames from informative ones. Most methods to detect and classify these non-informative frames are based on the hand-engineered features. However, it is very tedious to design optimal hand-engineered features. In this paper, we explore the effectiveness of Convolutional Neural Network (CNN) to detect and classify these non-informative frames. The experimental results show that the proposed approaches are promising.","PeriodicalId":152443,"journal":{"name":"Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114067554","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: 14

Segmentation of Femoral Cartilage with a Hybrid Method Combining Voxel Classification and Active Appearance Models 结合体素分类和活动外观模型的混合方法分割股骨软骨

Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing Pub Date : 2018-10-11 DOI: 10.1145/3288200.3288208

C. Öztürk, S. Albayrak

{"title":"Segmentation of Femoral Cartilage with a Hybrid Method Combining Voxel Classification and Active Appearance Models","authors":"C. Öztürk, S. Albayrak","doi":"10.1145/3288200.3288208","DOIUrl":"https://doi.org/10.1145/3288200.3288208","url":null,"abstract":"In this paper a hybrid segmentation method was proposed for delineation of the femoral cartilage compartment in knee MR images. A formerly developed voxel classification-based segmentation system was combined with an active appearance model (AAM) based segmentation system with an aim to solve the oversegmentation problems of purely classification-based approaches. The voxel classification-based segmentation was achieved through region-growing of sampled voxels depending on one-versus-all classifiers that used approximate nearest neighbour algorithm. Before the appearance model construction, dense set of correspondences were determined on the surfaces of cartilage atlases in 10 training MR images through an iterative shape-context-based non-rigid registration approach. Then, the appearance models for femoral cartilage compartment was constructed either using all of 10 training atlases or grouping these atlases as large and small depending on the physical examination information of the participants. The experimental analyses involved a comparative evaluation of the accuracies of these different AAM-based segmentations both individually and in combination with the voxel classification-based segmentations in 23 testing MR images as well as assessment of the misclassifications of the former segmentation system. As a result, the correspondence finding procedure worked successfully on the training atlases, and the hybrid segmentations with grouped appearance models achieved the closest accuracies to those of the voxel classification-based segmentation. The hybrid segmentations with appearance models that depended on the patient-specific information were evaluated as the most likely method to improve the segmentation accuracies with removal of oversegmented cartilage compartments.","PeriodicalId":152443,"journal":{"name":"Proceedings of the 2018 3rd International Conference on Biomedical Imaging, Signal Processing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130649737","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