Retinal spot lesion detection using adaptive multiscale morphological processing

Authors:
Xin Zhang;Guoliang Fan
Affiliations:
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK;School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Year:
2006

Citing 5
Cited 0

Pattern Spectrum and Multiscale Shape Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus

Computers and Biomedical Research
An edge preserving noise smoothing technique using multiscale morphology

Signal Processing
Top-Down and Bottom-Up Strategies in Lesion Detection of Background Diabetic Retinopathy

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Model-based edge reconstruction for low bit-rate wavelet-compressed images

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new spot lesion detection algorithm for retinal images with background diabetic retinopathy (DR) pathologies. The highlight of this algorithm is its capability to deal with all DR-related spot lesions of various sizes and shapes that is accomplished by a unique adaptive multiscale morphological processing technique. A scale map is generated to delineate lesion areas based an edge model, and it is used to fuse multiscale morphological processing results for lesion enhancements. The local/releative entropy thresholding techniques are employed to segment lesion regions, and a scale-guided validation process is used to remove over-detections based on the scale map. The proposed algorithm is tested on 30 retinal images where all spot lesions are hand-labelled for performance evaluation. Compared with two existing algorithms, the proposed one significantly improves the overall performance of spot lesion detection producing higher sensitivity and/or predictive values.