AUTOMATED CHARACTERIZATION AND DETECTION OF DIABETIC RETINOPATHY USING TEXTURE MEASURES

2015 ◽  
Vol 15 (04) ◽  
pp. 1550045 ◽  
Author(s):  
MUTHU RAMA KRISHNAN MOOKIAH ◽  
JEN HONG TAN ◽  
CHUA KUANG CHUA ◽  
E. Y. K. NG ◽  
AUGUSTINUS LAUDE ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Morphological Changes ◽  
Risk Index ◽  
Performance Measure ◽  
Kernel Functions ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Screening Programs

The chronic and uncontrolled diabetes mellitus (DM) damages the retinal blood vessels leading to diabetic retinopathy (DR). The advanced stage of DR leads to loss of vision and subsequently blindness. The morphological changes during the progression of DR can be diagnosed using digital fundus images. The pathological changes in the retina influence the variations in pixel patterns which can be quantified using texture measures. In this paper, we have explored different texture measures namely statistical moments, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), local binary pattern (LBP), laws mask energy (LME), fractal dimension (FD), fourier spectrum (FS) and Gabor wavelet to characterize and classify the normal and DR classes. We have tabulated 109 texture parameters for the normal and DR classes. Further, these features were subjected to empirical receiver operating characteristic (ROC) based ranking to select optimal feature set. The ranked nested features were fed to the support vector machine (SVM) classifier with different kernel functions to evaluate the highest performance measure using the least number of features to discriminate normal and DR classes. Our proposed system was evaluated using two different databases Kasturba Medical College Hospital (KMCH) and Tan Tock Seng Hospital (TTSH), each with 340 images (170 normal and 170 DR). We have also formulated an integrated index called as diabetic retinopathy risk index (DRRI) using selected texture features to discriminate normal and DR classes using single number. The proposed frame work can be used to help the clinicians and also for mass DR screening programs.

scholarly journals A Kernel-Based Approach for Biomedical Named Entity Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rakesh Patra ◽  
Sujan Kumar Saha
Keyword(s):  
Kernel Function ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Kernel Functions ◽  
Entity Recognition ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Named Entity ◽  
Tree Kernel

Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

scholarly journals A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

scholarly journals Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma

2020 ◽  
Vol 9 (7) ◽  
pp. 2156
Author(s):  
Mi-ri Kwon ◽  
Jung Hee Shin ◽  
Hyunjin Park ◽  
Hwanho Cho ◽  
Eunjin Kim ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Distant Metastasis ◽  
Area Under The Curve ◽  
Distant Metastases ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Thyroid Ultrasound ◽  
Widely Invasive ◽  
Radiomics Signature

We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis (p < 0.05). Radiomics signature and widely invasive histology were significantly associated with distant metastasis on multivariate analysis (p < 0.01 and p = 0.003). The classifier using the results of the multivariate analysis showed an AUC of 0.93. The radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis of FTC.

scholarly journals RESEARCH ON CROP INFORMATION EXTRACTION OF AGRICULTURAL UAV IMAGES BASED ON BLIND IMAGE DEBLURRING TECHNOLOGY AND SVM

2021 ◽  
pp. 33-42
Author(s):  
Zehai Xu ◽  
Haiyan Song ◽  
Zhiming Wu ◽  
Zefu Xu ◽  
Shifang Wang
Keyword(s):  
Information Extraction ◽  
Recognition Rate ◽  
Objective Evaluation ◽  
Kernel Functions ◽  
The Other ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Evaluation Indexes ◽  
Crop Classification

The blurring of crop images acquired by agricultural Unmanned Aerial Vehicle (UAV) due to sudden inputs by operators, atmospheric disturbance, and many other factors will eventually affect the subsequent crop identification, information extraction, and yield estimation. Aiming at the above problems, the new proposed combined deblurring algorithm based on the re-weighted graph total variation (RGTV) and L0-regularized prior, and the other two representative deblurring algorithms were applied to restore blurry crop images acquired during UAV flight, respectively. The restoration performance was measured by subjective vision, and objective evaluation indexes. The crop shape-related and texture-related feature parameters were then extracted, the Support Vector Machine (SVM) classifier with four common kernel functions was implemented for crop classification to realize the purpose of crop information extraction. The deblurring results showed that the proposed algorithm performed better in suppressing the ringing effect and preserving the image fine details, and retained higher objective evaluation indexes than the other two deblurring algorithms. The comparative analysis of different classification kernel functions showed that the Polynomial kernel function with an average recognition rate of 94.83% was most suitable for crop classification and recognition. The research will help in further popularization of crop monitoring based on UAV low-altitude remote sensing.

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 Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

Performance Evaluation of Spectrogram Based Epilepsy Detection Techniques Using Gray Scale Features

2020 ◽  
pp. 1-7
Author(s):  
Sunandan Mandal ◽  
Kavita Thakur ◽  
Bikesh Kumar Singh ◽  
Heera Ram
Keyword(s):  
Performance Evaluation ◽  
Human Error ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Gray Scale ◽  
Detection Techniques ◽  
Detection Model ◽  
Data Division

Electroencephalogram (EEG) is most common instrument for treatment and diagnosis of brain related diseases. Analysis of EEG signals for treatment of patient is time consuming and not easy task for neurologist. There is always a chance of human error. The purpose of this paper is to present an automatic detection model for epileptic seizure from EEG signals. To fulfill this objective, EEG signals are preprocessed and converted into spectrogram images using Short Time Fourier Transform (STFT). From this spectrogram images gray scale features are extracted. Support Vector Machine (SVM) with six different kernel functions and three data division protocols are utilized for performance evaluation of proposed model. Results show that quadratic SVM classifier has achieved highest classification accuracy.

scholarly journals An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

2018 ◽  
Vol 7 (2.15) ◽  
pp. 154 ◽  
Author(s):  
Fanji Ari Mukti ◽  
C Eswaran ◽  
Noramiza Hashim ◽  
Ho Chiung Ching ◽  
Mohamed Uvaze Ahamed Ayoobkhan
Keyword(s):  
Diabetic Retinopathy ◽  
Hierarchical Classification ◽  
Curvelet Transform ◽  
Automated System ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Hard Exudates ◽  
Automated Grading ◽  
Fast Discrete Curvelet Transform

In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%. 

scholarly journals Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yawen Liu ◽  
Haijun Niu ◽  
Jianming Zhu ◽  
Pengfei Zhao ◽  
Hongxia Yin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Healthy Subjects ◽  
Morphological Changes ◽  
Gray Matter Volume ◽  
Brain Regions ◽  
Classification Model ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Svm Classifier ◽  
Neuroimaging Biomarkers

According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphological neuroimaging biomarkers that may characterize idiopathic tinnitus using machine learning methods. Forty-six patients with idiopathic tinnitus and fifty-six healthy subjects were included in this study. For each subject, the gray matter volume of 61 brain regions was extracted as an original feature pool. From this feature pool, a hybrid feature selection algorithm combining the F-score and sequential forward floating selection (SFFS) methods was performed to select features. Then, the selected features were used to train a support vector machine (SVM) model. The area under the curve (AUC) and accuracy were used to assess the performance of the classification model. As a result, a combination of 13 cortical/subcortical brain regions was found to have the highest classification accuracy for effectively differentiating patients with tinnitus from healthy subjects. These brain regions include the bilateral hypothalamus, right insula, bilateral superior temporal gyrus, left rostral middle frontal gyrus, bilateral inferior temporal gyrus, right inferior parietal lobule, right transverse temporal gyrus, right middle temporal gyrus, right cingulate gyrus, and left superior frontal gyrus. The accuracy in the training and test datasets was 80.49% and 80.00%, respectively, and the AUC was 0.8586. To the best of our knowledge, this is the first study to elucidate brain morphological changes in patients with tinnitus by applying an SVM classifier. This study provides validated cortical/subcortical morphological neuroimaging biomarkers to differentiate patients with tinnitus from healthy subjects and contributes to the understanding of neuroanatomical alterations in patients with tinnitus.

scholarly journals Security Challenges and Application for Underwater Wireless Sensor Network

10.29007/ctsn ◽  
2018 ◽  
Author(s):  
Sarvesh Kumar Kumar ◽  
Bersha Kumari ◽  
Harshita Chawla
Keyword(s):  
Ct Images ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Normal Brain ◽  
Brain Images ◽  
Brain Ct ◽  
Automated Method ◽  
Occurrence Matrix

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

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