Screening programs in high-risk populations constitute a major asset in the struggle against Breast Cancer. Currently, screening programs focus in the task of analyzing digital mammography images. In this sense, computer vision techniques are suitable to provide decisive help in this task. In particular, computer-based texture image analysis is an important discipline that is able to gather some evidences oriented to the early diagnosis of breast cancer, such as the analysis of mammographic density. In order to extract textural features, Gabor Filters have been extensively used. The image is filtered with a set of Gabor Filters having different frequencies, resolutions and orientations. In this paper, we address the problem of mammogram images analysis by means of a Gabor Filter bank. Specifically, we analyze the texture features provided by the Gabor Filter bank in three regions, namely: tumor region, tumor-border region, and normal tissue region. An important objective is to reach a suitable subset of Gabor Filters that produce a collection of texture features sufficiently different to distinguish among the three regions. In this work, we have used the Choquet integral operator in order to score each filter in the bank, giving thus the possibility to select the most appropriate Gabor Filters to face the task of identifying relevant features for each of the three regions mentioned above. A learning procedure based on optimization is used to find the appropriate parameters for the Choquet integral, taking into account some training examples and constraints.