Automatic Exudates Detection in Diabetic Retinopathy Images

2016 ◽  
Vol 5 (2) ◽  
pp. 45
Author(s):  
H. Faouzi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Vascular Lesions ◽  
Edge Detection Algorithm ◽  
Invariant Moments ◽  
Recognition Phase ◽  
Prompt Diagnosis

Diabetic Retinopathy (DR) refers to the presence of typical retinal micro vascular lesions in persons with diabetics. When the disease is at the early state, a prompt diagnosis may help in preventing irreversible damages to the diabetic eye. If the exudates are closer to macula, then the situation is critical. Early detection can potentially reduce the risk of blind.  This paper proposes tool for the early detection of Diabetic Retinopathy using edge detection, algorithm kmeans in segmentation phase, invariant moments (Hu and Affine) and descriptor GIST in extraction phase. In the recognition phase, neural network is adopted. All tests are applied on database DIARETDB1.

scholarly journals Artificial Neural Network Based Fast Edge Detection Algorithm for MRI Medical Images

2017 ◽  
Vol 7 (1) ◽  
pp. 123 ◽  
Author(s):  
Teddy Surya Gunawan ◽  
Iza Zayana Yaacob ◽  
Mira Kartiwi ◽  
Nanang Ismail ◽  
Nor Farahidah Za'bah ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Quality ◽  
Edge Detection ◽  
Medical Image ◽  
Medical Images ◽  
Detection Algorithm ◽  
Edge Detector ◽  
Edge Detection Algorithm ◽  
Canny Edge

<p>Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector.</p>

scholarly journals Iris Recognition Using Image Moments and k-Means Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yaser Daanial Khan ◽  
Sher Afzal Khan ◽  
Farooq Ahmad ◽  
Saeed Islam
Keyword(s):  
Edge Detection ◽  
Euclidean Distance ◽  
Iris Recognition ◽  
Feature Vector ◽  
Detection Algorithm ◽  
Grayscale Image ◽  
Translation Invariant ◽  
Edge Detection Algorithm ◽  
Invariant Moments ◽  
Rectangular Form

This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%.

Sign in / Sign up

Export Citation Format

Share Document