Abnormality detection in retinal image by individualized background learning. (June 2020) - Chen, Benzhi; Wang, Lisheng; Wang, Xiuying; Sun, Jian; Huang, Yijie; Feng, Dagan; Xu, Zongben
Highlights: A multi-scale sparse coding based learning algorithm is proposed for effectively learning the individualized retinal background. A repeated learning strategy is proposed for improving the accuracy of the individualized retinal background. A feasible approach is developed for detecting both salient and weak retinal lesions. Abstract: Computer-aided lesion detection (CAD) techniques, which provide potential for automatic early screening of retinal pathologies, are widely studied in retinal image analysis. While many CAD approaches based on lesion samples or lesion features can well detect pre-defined lesion types, it remains challenging to detect various abnormal regions (namely abnormalities) from retinal images. In this paper, we try to identify diverse abnormalities from a retinal test image by finely learning its individualized retinal background (IRB) on which retinal lesions superimpose. 3150 normal retinal images are collected as the priors for IRB learning. A preprocessing step is applied to all retinal images for spatial, scale and color normalization. Retinal blood vessels, which have individual variations in different images, are particularly suppressed from all images. A multi-scale sparse coding based learning (MSSCL) algorithm and a repeated learning strategy are proposed for finely learning the IRB. By the MSSCL algorithm, a background space is constructed by sparsely encoding the test image in a multi-scale manner using the dictionary learned from normal retinal images, which will contain more complete IRB information than any single-scale coding result. From the background space, the IRB can be well learned by low-rank approximation and thus different salient lesions can be separated and detected. The MSSCL algorithm will be iteratively repeated on the modified test image in which the detected salient lesions are suppressed, so as to further improve the accuracy of the IRB and suppress lesions in the IRB. Consequently, a high-accuracy IRB can be learned and thus both salient lesions and weak lesions that have low contrasts with the background can be clearly separated. The effectiveness and contributions of the proposed method are validated by experiments over different clinical data-sets and comparisons with the state-of-the-art CAD methods.