The retina as a neuromimetic model to extract data in noisy images : application to detection of microcalcification clusters in mammography.

The nervous system is a powerful information processing machine, especially for vision. Neuromimetic methods try to extract some of the most powerful strategies of the neural system to apply them to help to solve delicate engineering problems. We developed such a method to extract images hidden into noisy background. This method mimics one characteristic of the retina which is a sensor that automatically adapts to the image characteristics and realizes outlines extraction and adaptative filtering, based on its network properties. We applied this method to detect automatically the clusters of microcalcifications in mammographies. Results were tested using the standardized data set DDSM, designed to test the automatic detection methods. We show that our "retina" can extract most of the microcalcifications that can be grouped together into clusters.