Content-based image retrieval (CBIR) is the process of searching digital images in a large database based on features, such as color, texture and shape of a given query image. As many images are compressed by transforms, constructing the feature vector directly in transform domain is a very popular topic. Therefore, features can be extracted directly from images in compressed format by using, for example, discrete cosine transform (DCT) for JPEG compressed images. Also, region-based image retrieval (RBIR) has attracted great interest in recent years. This paper proposes a new RBIR approach using shape-adaptive discrete cosine transform (SA-DCT). In this retrieval system, an image has a prior segmentation alpha plane, which is defined exactly as in MPEG-4. Therefore, an image is represented by segmented regions, each of which is associated with a feature vector derived from DCT and SA-DCT coefficients. Users can select any region as the main theme of the query image. The similarity between a query image and any database image is ranked according to a same similarity measure computed from the selected regions between two images. For those images without distinctive objects and scenes, users can still select the whole image as the query condition. The experimental results show that the proposed approach is able to identify main objects and reduce the influence of background in the image, and thus improve the performance of image retrieval in comparison with a conventional CBIR based on DCT.