Diabetic retinopathy (DR) is a complication of diabetes that causes blindness to 1.8 million people in the world. The risk of vision loss from DR increases when pathologies present on the macula. In this paper, we present an automatic system to detect pathologies on the macula such as hard exudates microaneurysms, and hemorrhages. Our approach is a bottom-up implementation, which tries to capture each abnormal structure in the macula in order to detect DR lesions. This technique starts by eliminating the non-uniform illumination thereby enhancing the contrast of red lesions in the images. Possible DR lesion (hard exudates and red lesions) candidates on the macula are extracted by using amplitude-modulation frequency-modulation (AM-FM) features. AM-FM features extract texture information from different frequency scales, providing for an effective method for the detection of hard exudates and red lesions. For each lesion candidate, we also extract shape, color and other texture features that are then combined with AM-FM features. Pathologies in the macula are detected from the candidate lesions using supervised classification with Partial Least Squares.