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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Rana, Shailja Jain, Shruti Virmani, Jitendra Jaypee Univ Informat Technol Waknaghat Himachal Prades India Thapar Univ Patiala Punjab India

ISBN: (纸本)9789380544199

In the present work, characterization of primary benign and primary malignant focal kidney lesions has been carried out using Gabor wavelet texture features. The angiomyolipomas (AMLs) i.e., primary benign kidney lesions and renal cell carcinomas (RCCs) i.e., primary malignant kidney lesions have been considered in the present study. The Gabor features have been computed from region of interest (ROIs) extracted from each image. The total dataset consists of 101ROIs (23 AML ROIs and 78 RCC ROIs) that have been extracted from within the lesions (ignoring the renal sinus part). For the classification task, support vector machine (SVM) has been used. It is observed that the proposed system yields an overall classification accuracy of 90.0 % for differential analysis between primary benign and primary malignant kidney lesions.

关键词： Computer aided diagnosis system Focal kidney lesions Gabor wavelet features Kidney ultrasound images Primary benign lesion Primary malignant lesion support vector machine classifier

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Diabetic Retinopathy Related Lesions Detection and Classification Using machine Learning Technology 15th

Diabetic Retinopathy Related Lesions Detection and Classific...

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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Saha, Rituparna Chowdhury, Amrita Roy Banerjee, Sreeparna West Bengal Univ Technol Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9783319393841

A novel Computer Aided Diagnosis System for early diagnosis of Diabetic Retinopathy is proposed for the detection and classification of Bright lesion classes and Dark lesion classes of Fundus Retina images using machine learning mechanisms. In the proposed methodology, the detection procedure is based on Fuzzy C Means (FCM) clustering technique to segment the candidate region areas. In the Dark lesion category, attempts are being made to modify the Micro aneurysms detection and Blood vessel elimination with the help of improvised algorithms. For the classification of each Bright and Dark lesion classes a classification system is built using machine learning algorithms namely Naive Bayes and support vector machine. A comparative study between the two machine learning algorithms yield accuracy of 97.0588% for Bright lesion classification using Naive Bayes classifier and accuracy of 88.8889% for Dark lesion classification using support vector machine classifier.

关键词： Diabetic retinopathy Fuzzy C means Nave Bayes classifier support vector machine classifier

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Integrating recursive Bayesian estimation with support vector machine to map probability of flooding from multispectral Landsat data

Integrating recursive Bayesian estimation with support vecto...

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Sarker, Chandrama Alvarez, Luis Mejias Woodley, Alan Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

ISBN: (纸本)9781509028962

This paper addresses the challenge of introducing a Bayes rules to measure flood probability from multispectral data. machine learning classifiers were applied to map the extent of flood inundation from multispectral remote sensing imagery. The paper applies Extended support vector machine classifier along with linear spectral un-mixing to obtain the classification output. K-means clustering is applied on pre and post flood images to select SVM training samples from clustering outcome of the most informative spectral band. Experiments were conducted by dividing training and testing samples into two groups. The efficiency of classifier was enhanced by introducing the Bayesian probability measure and performance was assessed by using precision and recall metrics on the pre and post Bayesian flood probability estimation. It has been observed that for some test cases in this study there was a substantial improvement in precision-recall curve with high precision values and low recall rate. The optimal flood probability threshold value has also been easily calculated by calculating and analising iteratively precision and recall.

关键词： remote sensing K-means clustering support vector machine classifier Bayesian estimation

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Hybrid EEG-EOG System for Intelligent Prosthesis Control based on Common Spatial Pattern Algorithm

Hybrid EEG-EOG System for Intelligent Prosthesis Control bas...

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IEEE International Conference on Information and Automation

作者： Junyou Yang Xiaoying Su Dianchun Bai Yinlai Jiang Hiroshi Yokoi Department of Electrical Engineering Shenyang University of Technology Shenyang Liaoning Province China Department of Intelligent Robotics The University of Electro-Communications Chofu Tokyo Japan

ISBN: (纸本)9781509041039

A newly brain computer interaction (BCI) system which combined electrooculography (EOG) with electroencephalography (EEG) was designed and realized to make prosthesis control with multicontrol commands come true. Based on two different imaginary tasks, time-frequency spectrum and brain topographic mapping were used to analyze and validate event related synchronization/desychronization (ERS/ERD). Obviously frequency band of EEG is chosen based on common spatial pattern (CSP) algorithm, and then support vector machine (SVM) were implemented to classify data's characteristics. In order to improve the performance of BCI system, the EOG signals caused by eye movements and EEG signals of ERS/ERD were simultaneously provided as the input of hybrid BCI system In the lab environment, the proposed system was tested on a multi-functional robotic prosthesis. The robot is light in weight and low in power consumption. Taking advantage of both EOG and EEG, recognition accuracy of entire system reaches 91.1%. The experiments results show that feature information of EEG signal which is evoked by motor imagery have obvious spatial specificity. This hybrid system can recognize different motions of the upper limb, whereas the task can be executed effectively and operator controls the prosthesis easily as their own arm.

关键词： EOG signal EEG signal prosthesis common spatial pattern support vector machine classifier

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Research on biomechanical compatibility for the artificial anal sphincter based on rectal perception function reconstruction

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2015年第8期9卷 921-927页

作者： Zan, Peng Zhao, Jie Yang, Lihong Shanghai Univ Sch Mechatron Engn & Automat Dept Automat Shanghai 200072 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

To treat the anal incontinence, a new artificial anal sphincter is put forward. Aiming at the rectal perception function reconstruction and the mechanical compatibility for artificial anal sphincter, this paper regards the huge mobile contraction wave in the typical rectal pressure contraction waves as the main signal of defecation. Meanwhile, by extracting features of rectal pressure signals with the help of wavelet packet analysis, and by adopting the Davies Bouldin index (DB) optimal algorithm, rectal perception function reconstruction is realised through predicting the defecation by means of the SVM classifier. In order to avoid the rectal ischemia necrosis, a three-dimensional finite element model for the rectum and artificial anal sphincter is constructed, with the purpose of analysing the viscoelasticity of the rectum. Then, prony series is deduced by utilising the constitutive equation of rectum, and finite element analysis for artificial anal sphincter is conducted by simulating the contraction and relaxation of the sphincter to generate load. At last, the shift and the stress distribution for the rectum at the time of being oppressed are obtained. This simulative experiment result shows that the artificial anal sphincter can highly accurately predict the defecation and it features favorable biomechanical compatibility.

关键词： biomechanics biological organs support vector machines feature extraction finite element analysis prosthetics viscoelasticity biomechanical compatibility artificial anal sphincter rectal perception function reconstruction feature extraction wavelet packet analysis DB optimal algorithm support vector machine classifier three-dimensional finite-element model rectum viscoelasticity Prony series stress distribution anal incontinence

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TerraSAR-X dual-pol time-series for mapping of wetland vegetation

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2015年第Sep.期107卷 90-98页

作者： Betbeder, Julie Rapinel, Sebastien Corgne, Samuel Pottier, Eric Hubert-Moy, Laurence Univ Rennes 2 LETG Rennes COSTEL UMR CNRS OSUR 6554 F-35043 Rennes France ECOBIO UMR CNRS 6553 OSUR F-35042 Rennes France Univ Rennes 1 IETR UMR CNRS 6164 F-35042 Rennes France

Mapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using support vector machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restorat

关键词： Wetlands Synthetic Aperture Radar (SAR) TerraSAR-X imagery Multi-temporal classifications support vector machine classifier

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Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2015年第3期64卷 205-215页

作者： Kotu, Lasya Priya Engan, Kjersti Borhani, Reza Katsaggelos, Aggelos K. Orn, Stein Woie, Leik Eftestol, Trygve Univ Stavanger Dept Elect Engn & Comp Sci N-4036 Stavanger Norway Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA Stavanger Univ Hosp Dept Cardiol N-4011 Stavanger Norway

Introduction: Patients surviving myocardial infarction (MI) can be divided into high and low arrhythmic risk groups. Distinguishing between these two groups is of crucial importance since the high-risk group has been shown to benefit from implantable cardioverter defibrillator insertion;a costly surgical procedure with potential complications and no proven advantages for the low-risk group. Currently, markers such as left ventricular ejection fraction and myocardial scar size are used to evaluate arrhythmic risk. Methods: In this paper, we propose quantitative discriminative features extracted from late gadolinium enhanced cardiac magnetic resonance images of post-MI patients, to distinguish between 20 high-risk and 34 low-risk patients. These features include size, location, and textural information concerning the scarred myocardium. To evaluate the discriminative power of the proposed features, we used several built-in classification schemes from matrix laboratory (MATLAB) and Waikato environment for knowledge analysis (WEKA) software, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest. Results: In Experiment 1, the leave-one-out cross-validation scheme is implemented in MATLAB to classify high- and low-risk groups with a classification accuracy of 94.44%, and an AUC of 0.965 for a feature combination that captures size, location and heterogeneity of the scar. In Experiment 2 with the help of WEKA, nested cross-validation is performed with k-NN, SVM, adjusting decision tree and random forest classifiers to differentiate high-risk and low-risk patients. SVM classifier provided average accuracy of 92.6%, and AUC of 0.921 for a feature combination capturing location and heterogeneity of the scar. Experiment 1 and Experiment 2 show that textural features from the scar are important for classification and that localization features provide an additional benefit. Conclusion: These promising results suggest that the discri

关键词： Local binary pattern Sobel filter k-Nearest neighbor classifier support vector machine classifier Cardiac magnetic resonance image High and low arrhythmic risk

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Parameterization of Limbus Distance in Van Herick Method's Angle Closure Glaucoma Diagnosis

Parameterization of Limbus Distance in Van Herick Method's A...

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International Conference of Information and Communication Technology for Embedded Systems

作者： I. S. Ardakani W. Kongprawechnon T. Theeramunkong P. Bunnun T. Isshiki A. Manassakorn School of ICT Sirindhorn International Institute of Technology (SIIT) Thammasat University PathumThani Thailand Advanced Automation and Electronics Research Unit National Electronics and Computer Technology Center Pathum Thani Thailand Tokyo Institute of Technology Global Scientific Information and Computing Center ICT Support Division Authentication and Networking Group Dept. Communications and Computer Engineering Tokyo Japan Department of Ophthalmology Faculty of Medicine Chulalongkorn University and King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand

ISBN: (纸本)9781509022502

As a non-invasive angle closure glaucoma diagnosis procedure, Van Herick's slit-lamp Limbal Anterior Chamber Depth Estimation (LACDE) technique is preferably the current standard screening method preliminarily performed at hospitals and eye clinics. However, accuracy of depth estimation is very much dependent on the slit projection distance from the limbus (LD). This study investigated the relation and designed a parametric model to reduce the inconsistencies between results and improving the Van Herick's method accuracy. Additionally, two different types of classifiers such as support vector machine (SVM) and Decision Tree with addition of LD as a predictor were trained and tested for comparison with parametric model. In the end acquired results clearly showed the effectiveness of LD consideration in LACDE, which lead to improvement in the experimental results.

关键词： Limbal Anterior Chamber Depth Measurement Van Herick Method support vector machine classifier Decision Tree classifier

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SVM-based hyperspectral image classification using intrinsic dimension

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ARABIAN JOURNAL OF GEOSCIENCES 2015年第1期8卷 477-487页

作者： Hasanlou, Mahdi Samadzadegan, Farhad Homayouni, Saeid Univ Tehran Remote Sensing Div Surveying & Geomat Engn Dept Coll Engn Tehran *** Iran Univ Ottawa Dept Geog Ottawa ON K1N 6N5 Canada

Hyperspectral images currently have potential applications in many scientific areas due to their high spectral resolution and consequently their good information contents. Nevertheless, extracting suitable and adequate features from this data is crucial for any analysis and especially for the classification algorithms. To overcome this issue, dimension reduction techniques are proposed and have showed a direct effect on improving the classifier efficiency of hyperspectral images. One common approach to decreasing the dimensionality is feature extraction by considering the intrinsic dimensionality of the data. In this paper, using support vector machines (SVMs), we propose a procedure that improves feature extraction methods through estimating the appropriate dimension of the transformation space. Due to the nature of the SVM classifier that can efficiently handle the high-dimensional data, our procedure tunes and optimizes the input space data to achieve better output performance. This study presents an efficient method for classifying hyperspectral images with SVMs by considering a suitable dimension for extracted features. The results reveal the superiority of the implemented method to improve the overall accuracy of classified imagery by a margin of nearly 10 %.

关键词： Dimension reduction Intrinsic dimension estimation Feature extraction support vector machine classifier

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Regional landscape mapping through a method of chain standardization of Landsat images

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LANDSCAPE AND URBAN PLANNING 2015年 134卷 1-9页

作者： Meng, Qingmin Mississippi State Univ Dept Geosci Starkville MS 39762 USA

Large area landscape analysis and modeling are often based on the data of land cover categories, which represent the interface between natural conditions and human impacts over time and across space. This study proposes a method of imagery chain standardization, which is straightforward and simple for landscape scientists to do large area land cover analysis with commonly available Landsat data. Unlike image normalization methods using complicated atmospheric correction and radiometric conservation, chain standardization proposes that for a large area, one Landsat image locating around the center of this area is used as the reference image. The reference image is first used to standardize its nearest neighboring images by applying a simple linear regression equation without intercept, which is developed based on the overlap imagery areas. Likewise, the firstly standardized images are used as new reference images to standardize their neighboring images further away from the study area center, and till all images covering a study area are transformed to the standard of the reference image. We test imagery chain standardization by using support vector machine classifier to classify the land cover of the states of Louisiana and Arkansas, USA, into agriculture, barren lands, forest, urban, water, and wetlands. 66-scene commonly available Landsat TM images are standardized, which results in promising mosaics and classification performance with overall accuracy values about 90% and Kappa coefficients close to 1. The imagery chain standardization is a promising method for landscape scientists to do regional landscape mapping and modeling. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Regional landscape analysis Land cover mapping Imagery chain standardization Linear regression support vector machine classifier

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