论文标题
使用卷积神经网络比较自动化微型神经瘤检测中的不同血管分割方法
Comparison Different Vessel Segmentation Methods in Automated Microaneurysms Detection in Retinal Images using Convolutional Neural Networks
论文作者
论文摘要
图像处理技术为医生提供了重要的帮助，并在不同的任务中减轻了工作量。特别是，从图像中识别病变和解剖结构等感兴趣的对象是一个具有挑战性且迭代的过程，可以通过计算机化方法成功地完成。微型尿布（MAS）检测是视网膜图像分析算法中的关键步骤。 MAS检测的目的是在视网膜图像中找到糖尿病性视网膜病（DR）的进展和鉴定。这项研究的目的是应用三种视网膜血管分割方法，即高斯（Log）的Laplacian（log），Canny Edge检测器和匹配的过滤器，以使用在正常图像中或在DR存在的情况下的无监督和监督学习的组合来比较MAS检测的结果。算法的步骤如下：1）预处理和增强，2）血管分割和掩蔽，3）使用基于匹配的方法和卷积神经网络的组合使用MAS检测和定位。为了评估我们提出的方法的准确性，我们将方法的输出与眼科医生收集的基础真理进行了比较。通过使用日志血管分割，我们的算法在局部视网膜数据库中对MAS的100个颜色图像的检测中发现了超过85％的敏感性和40个公共数据集（驱动器）的图像。对于Canny容器进行分割，我们的自动化算法在两个数据库的所有140张图像中发现MAS的灵敏度超过80％。最后，使用匹配的过滤器，我们的算法发现在局部和驱动数据集中检测MAS的灵敏度超过87％。
Image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by computerized approaches in a successful manner. Microaneurysms (MAs) detection is a crucial step in retinal image analysis algorithms. The goal of MAs detection is to find the progress and at last identification of diabetic retinopathy (DR) in the retinal images. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian (LoG), Canny edge detector, and Matched filter to compare results of MAs detection using a combination of unsupervised and supervised learning either in the normal images or in the presence of DR. The steps for the algorithm are as following: 1) Preprocessing and Enhancement, 2) vessel segmentation and masking, 3) MAs detection and Localization using a combination of Matching based approach and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compared the output of our method with the ground truth that collected by ophthalmologists. By using the LoG vessel segmentation, our algorithm found a sensitivity of more than 85% in the detection of MAs for 100 color images in a local retinal database and 40 images of a public dataset (DRIVE). For the Canny vessel segmentation, our automated algorithm found a sensitivity of more than 80% in the detection of MAs for all 140 images of two databases. And lastly, using the Matched filter, our algorithm found a sensitivity of more than 87% in the detection of MAs in both local and DRIVE datasets.
