Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs

In this article, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalites: there are color photographs, green filtered ...

• Type: Journal
• Year: 2008
• Where: TMI
• Authors: Gwénolé Quellec, Mathieu Lamard, P. M. Josselin, Guy Cazuguel, Béatrice Cochener, Christian Roux
• Keywords: Automatic Method, Diabetic Retinopathy, TMI 2008, Wavelets