Centro Algoritmi, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal.
Centro Algoritmi, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal; ENERMETER, Parque Industrial Celeirós 2ªFase, Lugar de Gaião - Lotes 5/6, 4705-025 Aveleda, Braga, Portugal.
Artif Intell Med. 2014 Mar;60(3):179-88. doi: 10.1016/j.artmed.2013.12.005. Epub 2013 Dec 28.
Microaneurysms represent the first sign of diabetic retinopathy, and their detection is fundamental for the prevention of vision impairment. Despite several research attempts to develop an automated system to detect microaneurysms in fundus images, none has shown the level of performance required for clinical practice. We propose a new approach, based on a multi-agent system model, for microaneurysm segmentation.
A multi-agent based approach, preceded by a preprocessing phase to allow construction of the environment in which agents are situated and interact, is presented. The proposed method is applied to two available online datasets and results are compared to other previously described approaches.
Microaneurysm segmentation emerges from agent interaction. The final score of the proposed approach was 0.240 in the Retinopathy Online Challenge.
We achieved competitive results, primarily in detecting microaneurysms close to vessels, compared to more conventional algorithms. Despite these results not being optimum, they are encouraging and reveal that some improvements may be made.
微动脉瘤是糖尿病视网膜病变的第一个迹象，其检测对于预防视力损害至关重要。尽管已经有多项研究试图开发一种自动系统来检测眼底图像中的微动脉瘤，但没有一种系统达到了临床实践所需的性能水平。我们提出了一种新的方法，基于多智能体系统模型，用于微动脉瘤分割。
提出了一种基于多智能体的方法，该方法首先进行预处理阶段，以允许构建智能体所处和交互的环境。所提出的方法应用于两个可用的在线数据集，并将结果与其他先前描述的方法进行比较。
微动脉瘤分割是通过智能体交互实现的。在视网膜在线挑战赛中，该方法的最终得分为 0.240。
与更传统的算法相比，我们在检测靠近血管的微动脉瘤方面取得了有竞争力的结果。尽管这些结果不是最佳的，但它们令人鼓舞，并表明可以进行一些改进。
