Central serous chorioretinopathy (CSCR) is a common fundus disease. Early detection of CSCR is of great importance to prevent visual loss. Therefore, a novel automatic detection method is presented in this paper which integrates technologies including discrete wavelet transform (DWT) image decomposition, local binary patterns (LBP) based texture feature extraction, and multi-instance learning (MIL). LBP is selected due to its robustness to low contrast and low quality images, which can reduce the interference of image itself on the detection method. DWT image decomposition provides high-frequency components with rich details for extracting LBP texture features, which can remove redundant information that is not necessary for diagnosis of CSCR in the raw image. The tedious task of accurately locating and segmenting CSCR lesions is avoided by using MIL. Experiments on 358 optical coherence tomography (OCT) B-scan images demonstrate the effectiveness of our method. Even under the condition of single threshold, the accuracy of 99.58% is obtained at K = 35 by only using a high-frequency feature fusion scheme, which is competitive with the existing methods. Additionally, through further detail innovation, such as multi-threshold optimization (MTO) and integrated decision-making (IDM), the performance of our method is further improved and the detection accuracy is 100% at K = 40.

中枢浆液性脉络膜视网膜病变（CSCR）是一种常见的眼底疾病。尽早发现CSCR对于防止视力丧失非常重要。因此，本文提出了一种新颖的自动检测方法，该方法融合了包括离散小波变换（DWT）图像分解，基于局部二进制模式（LBP）的纹理特征提取和多实例学习（MIL）在内的技术。选择LBP是因为它对低对比度和低质量图像具有鲁棒性，可以减少图像本身对检测方法的干扰。DWT图像分解为提取LBP纹理特征提供了具有丰富细节的高频分量，这可以删除诊断原始图像中CSCR所不需要的冗余信息。通过使用MIL避免了准确定位和分割CSCR病变的繁琐任务。358光学相干断层扫描（OCT）B扫描图像的实验证明了我们方法的有效性。即使在单个阈值的条件下，在 仅使用高频特征融合方案，K = 35，与现有方法相比具有竞争优势。此外，通过进一步的细节创新，例如多阈值优化（MTO）和集成决策（IDM），我们的方法的性能得到了进一步提高，在K  = 40时检测精度为100％。