A challenge in Computer-Aided Diagnosis based on image exams is to provide a timely answer that complies to the specialist's expectation. In many situations, when a specialist gets a new image to analyze, having information and knowledge from similar cases can be very helpful. For example, when a radiologist evaluates a new image, it is common to recall similar cases from the past. However, when performing similarity queries to retrieve similar cases, the approach frequently adopted is to extract meaningful features from the images and searching the database based on such features. One of the most popular image feature is the gray-level histogram, because it is simple and fast to obtain, providing the global gray-level distribution of the image. Moreover, normalized histograms are also invariant to affine transformations on the image. Although vastly used, gray-level histograms generates a large number of features, increasing the complexity of indexing and searching operations. Therefore, the high dimensionality of histograms degrades the efficiency of processing similarity queries. In this paper we propose a new and efficient method associating the Shannon entropy and the gray-level histogram to considerably reduce the dimensionality of feature vectors generated by histograms. The proposed method was evaluated using a real dataset and the results showed impressive reductions of up to 99% in the feature vector size, at the same time providing a gain in precision of up to 125% in comparison with the traditional gray-level histogram.