Hierarchical method for cataract grading based on retinal images using improved Haar wavelet

by   Lvchen Cao, et al.

Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the improved Haar wavelet. A hierarchical strategy is used to transform the four-class classification problem into three adjacent two-class classification problems. Three sets of two-class classifiers based on a neural network are trained individually and integrated together to establish a complete classification system. The accuracies of the two-class classification (cataract and non-cataract) and four-class classification are 94.83 respectively. The performance analysis demonstrates that the improved Haar wavelet feature achieves higher accuracy than the original Haar wavelet feature, and the fusion of three sets of two-class classifiers is superior to a simple four-class classifier. The discussion indicates that the retinal image-based method offers significant potential for cataract detection.



There are no comments yet.


page 3

page 6

page 9

page 10

page 11

page 13

page 20

page 22


A Novel Retinal Vessel Segmentation Based On Histogram Transformation Using 2-D Morlet Wavelet and Supervised Classification

The appearance and structure of blood vessels in retinal images have an ...

Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification

We present a method for automated segmentation of the vasculature in ret...

Mexican Hat Wavelet Kernel ELM for Multiclass Classification

Kernel extreme learning machine (KELM) is a novel feedforward neural net...

Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus...

Automatic diagnosis of retinal diseases from color retinal images

Teleophthalmology holds a great potential to improve the quality, access...

Neural Networks with Manifold Learning for Diabetic Retinopathy Detection

Widespread outreach programs using remote retinal imaging have proven to...

Fundus2Angio: A Novel Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography

Carrying out clinical diagnosis of retinal vascular degeneration using F...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.