scholarly journals Identification of Mitral Annulus Hinge Point Based on Local Context Feature and Additive SVM Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Jianming Zhang ◽  
Yangchun Liu ◽  
Wei Xu
Keyword(s):  
Ultrasound Image ◽  
Mitral Annulus ◽  
Density Field ◽  
Local Context ◽  
Support Vector ◽  
Svm Classifier ◽  
Weighted Density ◽  
Hinge Point ◽  
Vector Machines ◽  
Hinge Points

The position of the hinge point of mitral annulus (MA) is important for segmentation, modeling and multimodalities registration of cardiac structures. The main difficulties in identifying the hinge point of MA are the inherent noisy, low resolution of echocardiography, and so on. This work aims to automatically detect the hinge point of MA by combining local context feature with additive support vector machines (SVM) classifier. The innovations are as follows: (1) designing a local context feature for MA in cardiac ultrasound image; (2) applying the additive kernel SVM classifier to identify the candidates of the hinge point of MA; (3) designing a weighted density field of candidates which represents the blocks of candidates; and (4) estimating an adaptive threshold on the weighted density field to get the position of the hinge point of MA and exclude the error from SVM classifier. The proposed algorithm is tested on echocardiographic four-chamber image sequence of 10 pediatric patients. Compared with the manual selected hinge points of MA which are selected by professional doctors, the mean error is in 0.96 ± 1.04 mm. Additive SVM classifier can fast and accurately identify the MA hinge point.

scholarly journals Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
R. J. Hemalatha ◽  
V. Vijaybaskar ◽  
T. R. Thamizhvani
Keyword(s):  
Active Contour ◽  
Performance Metrics ◽  
Ultrasound Image ◽  
Medical Image Segmentation ◽  
Similarity Criteria ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
Region Detection

Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

EFFECTS OF SVM PARAMETER OPTIMIZATION BASED ON THE PARAMETER DESIGN OF TAGUCHI METHOD

2011 ◽  
Vol 20 (03) ◽  
pp. 563-575 ◽  
Author(s):  
MEI LING HUANG ◽  
YUNG HSIANG HUNG ◽  
EN JU LIN
Keyword(s):  
Taguchi Method ◽  
Parameter Design ◽  
Support Vector ◽  
Svm Classifier ◽  
Liver Disorders ◽  
Second Stage ◽  
Vector Machines ◽  
Class Labels ◽  
Two Stages ◽  
Decision Boundaries

Support Vector Machines (SVMs) are based on the concept of decision planes that define decision boundaries, and Least Squares Support Vector (LS-SVM) Machine is the reformulation of the principles of SVM. In this study a diagnosis on a BUPA liver disorders dataset, is conducted LS-SVM with the Taguchi method. The BUPA Liver Disorders dataset includes 345 samples with 6 features and 2 class labels. The system approach has two stages. In the first stage, in order to effectively determine the parameters of the kernel function, the Taguchi method is used to obtain better parameter settings. In the second stage, diagnosis of the BUPA liver disorders dataset is conducted using the LS-SVM classifier; the classification accuracy is 95.07%; the AROC is 99.12%. Compared with the results of related research, our proposed system is both effective and reliable.

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

2010 ◽  
Vol 08 (01) ◽  
pp. 39-57 ◽  
Author(s):  
REZWAN AHMED ◽  
HUZEFA RANGWALA ◽  
GEORGE KARYPIS
Keyword(s):  
Markov Models ◽  
Structural Characteristics ◽  
Transmembrane Helix ◽  
3D Structure ◽  
Prediction Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Sequence Information ◽  
Vector Machines ◽  
Prediction Approach

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%–30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present TOPTMH, a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

scholarly journals Correlation Kernels for Support Vector Machines Classification with Applications in Cancer Data

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Jiang ◽  
Wai-Ki Ching
Keyword(s):  
Support Vector Machines ◽  
Positive Semidefinite ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Problems ◽  
Correlation Kernel ◽  
Cancer Data ◽  
Tumor Tissues ◽  
Vector Machines

High dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

A New SVM Kernel for Keyword Spotting Using Confidence Measures

2015 ◽  
Vol 24 (03) ◽  
pp. 1550010 ◽  
Author(s):  
Yassine Ben Ayed
Keyword(s):  
Support Vector Machines ◽  
Hidden Markov ◽  
Support Vector ◽  
Svm Classifier ◽  
Keyword Spotting ◽  
Confidence Measure ◽  
Confidence Measures ◽  
Vector Machines ◽  
Harmonic Means ◽  
Speech Recognizer

In this paper, we propose an alternative keyword spotting method relying on confidence measures and support vector machines. Confidence measures are computed from phone information provided by a Hidden Markov Model based speech recognizer. We use three kinds of techniques, i.e., arithmetic, geometric and harmonic means to compute a confidence measure for each word. The acceptance/rejection decision of a word is based on the confidence vector processed by the SVM classifier for which we propose a new Beta kernel. The performance of the proposed SVM classifier is compared with spotting methods based on some confidence means. Experimental results presented in this paper show that the proposed SVM classifier method improves the performances of the keyword spotting system.

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals AUTOMATED SHAPE-BASED PAVEMENT CRACK DETECTION APPROACH

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 598-608 ◽  
Author(s):  
Teng Wang ◽  
Kasthurirangan Gopalakrishnan ◽  
Omar Smadi ◽  
Arun K. Somani
Keyword(s):  
Crack Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
False Alarms ◽  
Polynomial Curve ◽  
Detection Approach ◽  
Vector Machines ◽  
Different Types ◽  
Surface Irregularities ◽  
Pavement Crack Detection

Pavements are critical man-made infrastructure systems that undergo repeated traffic and environmental loadings. Consequently, they deteriorate with time and manifest certain distresses. To ensure long-lasting performance and appropriate level of service, they need to be preserved and maintained. Highway agencies routinely employ semiautomated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by highway agencies for reporting and management purposes. We design a shape-based crack detection approach for pavement health monitoring, which takes advantage of spatial distribution of potential cracks. To achieve this, we first extract Potential Crack Components (PCrCs) from pavement images. Next, we employ polynomial curve to fit all pixels within these components. Finally, we define a Shape Metric (SM) to distinguish crack blocks from background. We experiment the shape-based crack detection approach on different datasets, and compare detection results with an alternate method that is based on Support Vector Machines (SVM) classifier. Experimental results prove that our approach has the capability to produce higher detections and fewer false alarms. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities.

Classifying Electroencephalogram (EEG) Signals Using BAT-SVM Classifier for Detecting Epilepsy

2021 ◽  
Vol 12 (3) ◽  
pp. 96-115
Author(s):  
Manal Tantawi ◽  
Aya Naser ◽  
Howida Shedeed ◽  
Mohammed Fahmy Tolba
Keyword(s):  
Support Vector Machines ◽  
Epileptic Seizures ◽  
Bat Algorithm ◽  
Line Length ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Source Of Information

Electroencephalogram (EEG) signals are a valuable source of information for detecting epileptic seizures. However, monitoring EEG for long periods of time is very exhausting and time consuming. Thus, detecting epilepsy in EEG signals automatically is highly appreciated. In this study, three classes, namely normal, interictal (out of seizure time), and ictal (during seizure), are considered. Moreover, a comparative study is provided for the efficient features in literature resulting in a suggested combination of only three discriminative features, namely R'enyi entropy, line length, and energy. These features are calculated from each of the EEG sub-bands. Finally, support vector machines (SVM) classifier optimized using BAT algorithm (BAT-SVM) is introduced by this study for discriminating between the three classes. Experiments were conducted using Andrzejak database. The accomplished experiments and comparisons in this study emphasize the superiority of the proposed BAT-SVM along with the suggested feature set in achieving the best results.

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