Abstract The cornea is an essential structure for the proper functioning of human vision. It can suffer injuries like tumors, areas of epithelial removal, infections, and post-surgical injuries that need prompt and effective treatment. The affected region’s area evolution monitoring enables the physician to evaluate the treatment effectiveness. This paper presents a semi-automatic method that can assist the physician in monitoring the evolution of corneal lesions. Our approach uses some specialist-marked regions to train a random forest classifier, and then to classify the other image areas as lesion or non-lesion. We extract color information, and, after classification, we apply an active contour operation to the most significant connected component. Our tests show that by marking 5% of the pixels, our method achieves an accuracy of 99.08% and a Dice of 0.85 on average. According to the literature, the segmentation in more than 90% of the images was considered excellent.

中文翻译：

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一种角膜病变的半自动分割方法

摘要 角膜是维持人类视觉正常运作的重要结构。它可能会遭受诸如肿瘤、上皮去除区域、感染和术后损伤等需要及时有效治疗的损伤。受影响区域的区域演变监测使医生能够评估治疗效果。本文提出了一种半自动方法，可以帮助医生监测角膜病变的演变。我们的方法使用一些专家标记的区域来训练随机森林分类器，然后将其他图像区域分类为病变或非病变。我们提取颜色信息，并在分类后对最重要的连接组件应用活动轮廓操作。我们的测试表明，通过标记 5% 的像素，我们的方法达到了 99 的准确率。08% 和平均 0.85 的骰子。根据文献，超过 90% 的图像的分割被认为是优秀的。