1. A process for identifying and quantifying helminth eggs in environmental samples, from at least one image comprising borders, wherein said process comprises the steps of: filtering the at least one image with an anisotropic diffusion filter to locate the borders on the at least one image, thereby obtaining at least one filtered image; filtering the at least one filtered image by applying at least one Laplacian of Gaussian threshold to detect changes within the at least one filtered image, thereby obtaining at least one binarized image; processing the at least one binarized image by means of a Watershed algorithm with a filtered distance for segmenting the at least one binarized image and extracting boundaries in the at least one binarized image, thereby obtaining at least one second filtered image; filtering the at least one second filtered image to eliminate objects by perimeter compactness, by considering the size of objects in the at least one second filtered image and separating the differences to avoid false positive, thereby obtaining at least one image with identified objects; characterizing the identified objects in the at least one image with identified objects by segmenting the objects by means of gray profiles, thereby obtaining characterized objects; classifying the characterized objects according to a statistic classifier for identifying and quantifying the type of helminth egg; and validating the result in a statistical manner; wherein the statistic classifier is a k neighbor classifier based on the Mahalanobis distance, for which k neighbor classifiers are used, one of morphological features and gray level and two based on Local Binary Pattern (LBP) texture descriptors; wherein the class of object will be determined as a function of a species to which the closest neighbors belong to, five neighbors with classic features, three with closest texture (LBP 4 ) and three with distance texture (LBP 8 ).