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[彩色眼底图像中微动脉瘤的自动检测]

[Automatic detection of microaneurysms in colour fundus images].

作者信息

Jiménez S, Alemany P, Núñez Benjumea F, Serrano C, Acha B, Fondón I, Carral F, Sánchez C

机构信息

Servicio de Oftalmología, Hospital Universitario Puerta del Mar, Cádiz, España.

出版信息

Arch Soc Esp Oftalmol. 2011 Sep;86(9):277-81. doi: 10.1016/j.oftal.2011.04.015. Epub 2011 Jul 29.

DOI:10.1016/j.oftal.2011.04.015
PMID:21893260
Abstract

PURPOSE

We present the development of a tool for the automatic detection of microaneurysms and its clinical evaluation. The intention of this tool is to facilitate the diagnosis of diabetic retinopathy in general screening programs.

METHOD

The designed and developed tool consists of three stages of processing: 1) Obtaining of the basic image of eye with the retinal camera, inverted image on the green channel, and a high-pass filter of the image. This phase enhances the microaneurysms. 2) Detection of the candidates for microaneurysms, by means of an adaptive prediction filter and regions growth. 3) Selection, among the candidates, of whom microaneurysms must be considered to fulfil the criteria of circular shape, high intensity in the inverted green channel and contrasts with respect to the surrounding pixels.

RESULTS

We selected to 20 retinal photographs of good quality and dimensions 600x600 pixels from patients with nonproliferative diabetic retinopathy. The ophthalmologists detected 297 microaneurysms in these images. The tool for automatic detection correctly located 252 microaneurysms, with a mean sensitivity of 89% and a false positives rate of 93%.

CONCLUSIONS

The results obtained seem to indicate that the tool developed will be very useful for its potential use in screening programs in primary care centres. On the other hand, more work is needed on the algorithm to decrease the rate of false positives.

摘要

目的

我们展示了一种用于自动检测微动脉瘤的工具的开发及其临床评估。该工具的目的是在一般筛查项目中促进糖尿病视网膜病变的诊断。

方法

设计并开发的工具包括三个处理阶段:1)使用视网膜相机获取眼睛的基本图像,在绿色通道上进行图像反转,并对图像进行高通滤波。此阶段增强了微动脉瘤。2)通过自适应预测滤波器和区域生长检测微动脉瘤候选者。3)在候选者中进行选择,对于那些必须符合圆形形状、绿色通道反转图像中高强度以及与周围像素有对比度标准的微动脉瘤。

结果

我们从非增殖性糖尿病视网膜病变患者中选择了20张质量良好且尺寸为600x600像素的视网膜照片。眼科医生在这些图像中检测到297个微动脉瘤。自动检测工具正确定位了252个微动脉瘤,平均灵敏度为89%,假阳性率为93%。

结论

获得的结果似乎表明,开发的工具对于其在基层医疗中心筛查项目中的潜在应用将非常有用。另一方面,需要对算法进行更多工作以降低假阳性率。

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