Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases

Yitian Zhao, Yalin Zheng, Yifan Zhao, Yonghuai Liu, Zhili Chen, Peng Liu, Jiang Liu

Abstract: Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesions in different types of images without the need of tuning parameters for different problems. To evaluate its effectiveness, we have applied our method to the detection lesions of retinal images. Four different types of lesions: exudate, hemorrhage, microaneurysms and leakage from 7 independent public retinal image datasets of diabetic retinopathy and malarial retinopathy, were studied and the experimental results show that the proposed method is superior to the state-of-the-art methods.