A Novel Efficient Approach for the Screening of New Abnormal Blood Vessels in Color Fundus Images

2014 ◽  
Vol 573 ◽  
pp. 808-813 ◽  
Author(s):  
B. Ramasubramanian ◽  
S. Selvaperumal
Keyword(s):  
Blood Vessels ◽  
Median Filter ◽  
Computation Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Blood Vessel Detection ◽  
Average Computation Time ◽  
Color Fundus Image ◽  
Color Fundus Images

Reliable detection of abnormal vessels in color fundus image is still a great issue in medical image processing. An Efficient and robust approach for automatic detection of abnormal blood vessels in digital color fundus images is presented in this paper. First, the fundus images are preprocessed by applying a 3x3 median filter. Then, the images are segmented using a novel morphological operation. To classify these segmented image into normal and abnormal, seven features based on shape, contrast, position and density are extracted. Finally, these features are classified using a non-linear Support Vector Machine (SVM) Classifier. The average computation time for blood vessel detection was less than 2.4sec with a success rate of 99%. The performance of our proposed method is measured on publically available DRIVE and STARE database.

scholarly journals Microaneurysms detection in color fundus images using machine learning based on directional local contrast

2020 ◽  
Author(s):  
Shengchun Long ◽  
Jiali Chen ◽  
Ante Hu ◽  
Haipeng Liu ◽  
Zhiqing Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Vessels ◽  
Delayed Diagnosis ◽  
Computation Time ◽  
Machine Learning Techniques ◽  
Connected Components ◽  
Screening Methods ◽  
Local Contrast ◽  
Fundus Images ◽  
Color Fundus Images

Abstract Background: As one of the major complications of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysms appear as the earliest symptom of DR. Accurate and reliable detection of microaneurysms in color fundus images has great importance for DR screening. Methods: A microaneurysms detection method using machine learning based on directional local contrast (DLC) is proposed for the early diagnosis of DR. First, blood vessels were enhanced and segmented using improved enhancement function based on analyzing eigenvalues of Hessian matrix. Next, with blood vessels excluded, microaneurysm candidate regions were obtained by using shape characteristics and connected components analysis. After image segmented to patches, the features of each microaneurysm candidate patch were extracted, and each candidate patch was classified into microaneurysm or non-microaneurysm. The main contributions of our study are: 1) Making use of directional local contrast in microaneurysms detection for the first time, which does make sense for better microaneurysms classification. 2) Applying three different machine learning techniques for classification and comparing their performance for microaneurysms detection. The proposed algorithm was trained and tested on e-ophtha MA database, and further tested on another independent DIARETDB1 database. Results of microaneurysms detection on the two databases were evaluated on lesion level and compared with existing algorithms. Results: The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 seconds, 3 seconds and 2.6 seconds respectively. Conclusions: The proposed method using machine learning based on directional local contrast of image patches can effectively detect microaneurysms in color fundus images and provide an effective scientific basis for early clinical DR diagnosis. Keywords: color fundus image; microaneurysms detection; patch; features extraction; directional local contrast; machine learning

Big Cats Classification Based on Body Covering

2021 ◽  
Vol 5 (5) ◽  
pp. 984-991
Author(s):  
Fernanda Januar Pratama ◽  
Wikky Fawwaz Al Maki ◽  
Febryanti Sthevanie
Keyword(s):  
Median Filter ◽  
Computation Time ◽  
Optimal Level ◽  
Input Image ◽  
The Body ◽  
Classification Model ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Animal Populations ◽  
Segmentation Image

The reduced habitat owned by an animal has a very bad impact on the survival of the animal, resulting in a continuous decrease in the number of animal populations especially in animals belonging to the big cat family such as tigers, cheetahs, jaguars, and others. To overcome the decline in the animal population, a classification model was built to classify images that focuses on the pattern of body covering possessed by animals. However, in designing an accurate classification model with an optimal level of accuracy, it is necessary to consider many aspects such as the dataset used, the number of parameters, and computation time. In this study, we propose an animal image classification model that focuses on animal body covering by combining the Pyramid Histogram of Oriented Gradient (PHOG) as the feature extraction method and the Support Vector Machine (SVM) as the classifier. Initially, the input image is processed to take the body covering pattern of the animal and converted it into a grayscale image. Then, the image is segmented by employing the median filter and the Otsu method. Therefore, the noise contained in the image can be removed and the image can be segmented. The results of the segmentation image are then extracted by using the PHOG and then proceed with the classification process by implementing the SVM. The experimental results showed that the classification model has an accuracy of 91.07%.  

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%. 

Telecommunication Traffic Prediction Based on Improved LSSVM

2017 ◽  
Vol 32 (03) ◽  
pp. 1850007 ◽  
Author(s):  
Jun-Xia Liu ◽  
Zhen-Hong Jia
Keyword(s):  
Communication Networks ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Computation Time ◽  
Traffic Prediction ◽  
Support Vector ◽  
Forecasting Model ◽  
Traffic Forecasting ◽  
Model Based ◽  
Average Computation Time

Telecommunication traffic prediction is an important aspect of data analysis and processing in communication networks. In this study, we utilize the least-squares support vector machine (LSSVM) prediction method to improve the prediction performance of telecommunication traffic. As the parameters of LSSVM are difficult to determine, we propose to optimize the LSSVM parameters using the improved artificial bee colony (IABC) algorithm based on the fitness-prediction strategy (i.e. FP-IABC). We employ real traffic data collected on site to establish a telecommunication traffic forecasting model based on FP-IABC optimizing LSSVM (FP-IABC-LSSVM). The experiment results indicate that in the case involving no increase in the computational complexity, the proposed telecommunication traffic forecasting model-based FP-IABC-LSSVM has a higher prediction accuracy than the prediction model based on the ABC optimizing LSSVM (ABC-LSSVM), particle swarm optimizing LSSVM (PSO-LSSVM), and genetic algorithm optimizing LSSVM (GA-LSSVM). Further, with respect to the standard root mean square error and the average computation time, the proposed FP-IABC-LSSVM is the optimal prediction method of all of the comparison methods. The proposed prediction method not only improves the prediction accuracy, but also reduces the average computation time.

scholarly journals Microaneurysms Detection in Color Fundus Images based on Naive Bayesian Classification

2020 ◽  
Author(s):  
Shengchun Long ◽  
Jiali Chen ◽  
Ante Hu ◽  
Haipeng Liu ◽  
Zhiqing Chen ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Hessian Matrix ◽  
Delayed Diagnosis ◽  
Major Complication ◽  
Bayesian Classification ◽  
Screening Methods ◽  
Fundus Images ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Color Fundus Images

Abstract Background: As a major complication of diabetes, diabetic retinopathy (DR) is a leading cause of visual impairment and blindness due to delayed diagnosis and intervention. Microaneurysm appears as the earliest symptom of DR. Accurate and reliable detection of microaneurysms in color fundus images has great importance for DR screening. Methods: A microaneurysms detection method based on Naive Bayesian classifier is proposed for the early diagnosis of DR. First, blood vessels were enhanced and segmented using the analysis of eigenvalues of Hessian matrix. Next, with blood vessels excluded, microaneurysm candidate regions were obtained according to shape characteristics. After image segmented to patches, the features of each microaneurysm candidate patch were extracted, and each candidate patch was classified using Naive Bayes into microaneurysm or non- microaneurysm. The proposed algorithm was trained and tested on e-ophtha MA database, and further tested on another independent DIARETDB1 database. Results of microaneurysms detection on the two databases were evaluated on lesion level and compared with existing algorithms. Results: The proposed method has achieved better performance compared with existing algorithms. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.845 and 0.831, respectively. The free-response ROC (FROC) score on the two databases was 0.362 and 0.207, respectively. Conclusions: The proposed method based on Naive Bayesian classification of image patches can effectively detect microaneurysms in color fundus images and provide an effective scientific basis for early clinical DR diagnosis.

scholarly journals Automatic detection and classification of diabetic retinopathy from retinal fundus images

2021 ◽  
Author(s):  
Abdullah Biran
Keyword(s):  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Automatic Detection ◽  
Histogram Equalization ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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