Automatic segmentation of blood vessels from retinal fundus images through image processing and data mining techniques

Abstract

Machine Learning techniques have been useful in almost every field of concern. Data Mining, a branch of Machine Learning is one of the most extensively used techniques. The ever-increasing demands in the field of medicine are being addressed by computational approaches in which Big Data analysis, image processing and data mining are on top priority. These techniques have been exploited in the domain of ophthalmology for better retinal fundus image analysis. Blood vessels, one of the most significant retinal anatomical structures are analysed for diagnosis of many diseases like retinopathy, occlusion and many other vision threatening diseases. Vessel segmentation can also be a pre-processing step for segmentation of other retinal structures like optic disc, fovea, microneurysms, etc. In this paper, blood vessel segmentation is attempted through image processing and data mining techniques. The retinal blood vessels were segmented through color space conversion and color channel extraction, image pre-processing, Gabor filtering, image postprocessing, feature construction through application of principal component analysis, k-means clustering and first level classification using Naïve-Bayes classification algorithm and second level classification using C4.5 enhanced with bagging techniques. Association of every pixel against the feature vector necessitates Big Data analysis. The proposed methodology was evaluated on a publicly available database, STARE. The results reported 95.05% accuracy on entire dataset; however the accuracy was 95.20% on normal images and 94.89% on pathological images. A comparison of these results with the existing methodologies is also reported. This methodology can help ophthalmologists in better and faster analysis and hence early treatment to the patients.

A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

“…Various other techniques such as sliding window technique [ 91 ], Multi Resolution Gabor Transform [ 115 ], Gaussian kernels [ 144 ], intensity-based techniques [ 66 , 79 ], statistical classifier [ 5 , 61 ], Principal Component Analysis (PCA) [ 46 , 67 ], Singular Value Decomposition (SVD), Linear Discriminant Analysis (LDA), Semantic Image Transformation (SIT) [ 24 ], entropy-based backtracking approach [ 63 ], ON detection algorithm [ 133 ], deformable models [ 88 , 100 ] and Locally Statistical Active Contour Model with the Structure Prior (LSACM-SP) approach [ 146 ] are also used to accomplish the purpose of feature segmentation and extraction, for DR detection. DR classification is performed using Clustering [ 5 , 46 , 51 , 88 , 91 , 116 , 145 ], ensemble techniques [ 13 , 14 , 18 , 141 ], SVM [ 22 , 63 , 108 ], Sparse Representation Classifier (SRC) [ 71 ], Neural Networks [ 42 , 68 , 85 , 126 , 135 ], Random Forest Classifier (RFC) [ 61 , 62 , 125 ], SVM based hybrid classifier [ 5 ], Majority Voting (MV ) [ 53 ] etc. Supervised classification techniques such as KNN classification [ 37 , 93 ], Extreme Learning Machine (ELM) and Naive Bayes (NB) [ 17 ], Bayesian classifier [ 55 ], cascade Adaboost CNN classifier [ 8 ], Naïve–Bayes and Decision Tree (DT) C4.5 enhanced with bagging techniques [ 46 ], etc.…”

“…DR classification is performed using Clustering [ 5 , 46 , 51 , 88 , 91 , 116 , 145 ], ensemble techniques [ 13 , 14 , 18 , 141 ], SVM [ 22 , 63 , 108 ], Sparse Representation Classifier (SRC) [ 71 ], Neural Networks [ 42 , 68 , 85 , 126 , 135 ], Random Forest Classifier (RFC) [ 61 , 62 , 125 ], SVM based hybrid classifier [ 5 ], Majority Voting (MV ) [ 53 ] etc. Supervised classification techniques such as KNN classification [ 37 , 93 ], Extreme Learning Machine (ELM) and Naive Bayes (NB) [ 17 ], Bayesian classifier [ 55 ], cascade Adaboost CNN classifier [ 8 ], Naïve–Bayes and Decision Tree (DT) C4.5 enhanced with bagging techniques [ 46 ], etc. are used for DR detection.…”

Detection of Pulmonary Nodules in Thoracic CT images using Image Processing and Data Mining Techniques

“…A sample of CT image that reveals the large lung nodule (annotated) is shown in Figure 1. and data mining techniques [5] have been prominently utilized for automatic identification of lung nodules. In this work, image clustering and classification of candidate components is attempted to identify large nodules in thoracic CT images.…”