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.

中文翻译：

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基于个性化背景学习的视网膜图像异常检测

摘要 计算机辅助病变检测 (CAD) 技术为视网膜病变的自动早期筛查提供了潜力，在视网膜图像分析中得到了广泛研究。虽然许多基于病变样本或病变特征的 CAD 方法可以很好地检测预先定义的病变类型，但从视网膜图像中检测各种异常区域（即异常）仍然具有挑战性。在本文中，我们尝试通过精细学习视网膜病变叠加在其上的个性化视网膜背景（IRB）来识别视网膜测试图像中的各种异常。收集了 3150 张正常视网膜图像作为 IRB 学习的先验。预处理步骤应用于所有视网膜图像以进行空间、尺度和颜色标准化。视网膜血管，在不同图像中存在个体差异，从所有图像中都特别受到抑制。提出了一种基于多尺度稀疏编码的学习（MSSCL）算法和重复学习策略，用于精细学习 IRB。通过MSSCL算法，使用从正常视网膜图像中学习到的字典，以多尺度方式对测试图像进​​行稀疏编码构建背景空间，其中包含比任何单尺度编码结果更完整的IRB信息。从背景空间中，可以通过低秩近似很好地学习 IRB，从而可以分离和检测不同的显着病变。MSSCL算法将对检测到的显着病变被抑制的修改后的测试图像进​​行迭代重复，以进一步提高IRB的准确性，抑制IRB中的病变。最后，可以学习高精度的IRB，因此可以清楚地区分与背景对比度低的显着病变和弱病变。通过对不同临床数据集的实验以及与最先进的 CAD 方法的比较，验证了所提出方法的有效性和贡献。