论文标题
有效的基于内核的匹配滤波器方法，用于分割视网膜血管
Efficient Kernel based Matched Filter Approach for Segmentation of Retinal Blood Vessels
论文作者
论文摘要
视网膜血管结构包含有关肥胖，糖尿病，高血压和青光眼等疾病的信息。该信息在识别和治疗这些致命疾病方面非常有用。为了获得此信息，需要分割这些视网膜血管。已经给出了许多基于内核的方法来分割视网膜血管，但它们的核不适合容器概况导致性能差。为了克服这一点，已经提出了一种新的基于内核的匹配滤波器方法。新匹配的过滤器用于生成匹配的过滤器响应（MFR）图像。我们已将OTSU阈值方法应用于获得的MFR图像以提取血管。我们进行了广泛的实验，以选择建议的匹配滤镜内核的最佳参数值。提出的方法已在两个在线可用驱动器和凝视数据集上进行了检查和验证。所提出的方法的特异性分别为98.50％，98.23％和准确性95.77％，驱动器和凝视数据集的95.13％。获得的结果证实，所提出的方法的性能比其他方法更好。性能提高的原因是由于提出的适当的内核，该核与视网膜血管谱更准确。
Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been given for segmentation of retinal vessels but their kernels are not appropriate to vessel profile cause poor performance. To overcome this, a new and efficient kernel based matched filter approach has been proposed. The new matched filter is used to generate the matched filter response (MFR) image. We have applied Otsu thresholding method on obtained MFR image to extract the vessels. We have conducted extensive experiments to choose best value of parameters for the proposed matched filter kernel. The proposed approach has examined and validated on two online available DRIVE and STARE datasets. The proposed approach has specificity 98.50%, 98.23% and accuracy 95.77 %, 95.13% for DRIVE and STARE dataset respectively. Obtained results confirm that the proposed method has better performance than others. The reason behind increased performance is due to appropriate proposed kernel which matches retinal blood vessel profile more accurately.
