Venkatesan Ragav, Chandakkar Parag, Li Baoxin, Li Helen K
Arizona State University, Tempe, AZ 85281, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1462-5. doi: 10.1109/EMBC.2012.6346216.
All people with diabetes have the risk of developing diabetic retinopathy (DR), a vision-threatening complication. Early detection and timely treatment can reduce the occurrence of blindness due to DR. Computer-aided diagnosis has the potential benefit of improving the accuracy and speed in DR detection. This study is concerned with automatic classification of images with microaneurysm (MA) and neovascularization (NV), two important DR clinical findings. Together with normal images, this presents a 3-class classification problem. We propose a modified color auto-correlogram feature (AutoCC) with low dimensionality that is spectrally tuned towards DR images. Recognizing the fact that the images with or without MA or NV are generally different only in small, localized regions, we propose to employ a multi-class, multiple-instance learning framework for performing the classification task using the proposed feature. Extensive experiments including comparison with a few state-of-art image classification approaches have been performed and the results suggest that the proposed approach is promising as it outperforms other methods by a large margin.
所有糖尿病患者都有患糖尿病性视网膜病变（DR）的风险，这是一种威胁视力的并发症。早期检测和及时治疗可减少因DR导致的失明发生率。计算机辅助诊断在提高DR检测的准确性和速度方面具有潜在优势。本研究关注具有微动脉瘤（MA）和新生血管形成（NV）这两种重要DR临床特征的图像的自动分类。连同正常图像一起，这构成了一个三类分类问题。我们提出了一种经过修改的低维彩色自相关图特征（AutoCC），它在光谱上针对DR图像进行了调整。认识到有或没有MA或NV的图像通常仅在小的局部区域有所不同这一事实，我们建议采用多类、多实例学习框架，使用所提出的特征来执行分类任务。已经进行了广泛的实验，包括与一些先进的图像分类方法进行比较，结果表明所提出的方法很有前景，因为它比其他方法有很大优势。
