The high incidence of brain disease, especially brain tumor, has increased significantly in recent years. It is becoming more and more concernful to discover knowledge through mining medical brain image to aid doctors’ diagnosis. Image mining is the important branch of data mining. It is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Image clustering and similarity retrieval are two basilic parts of image mining. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. ISSP refer to the longest similar and continuous sub-patterns hidden in two objects each of which contains an image sequence. These patterns are significant in medical images because the similarity for two medical images is not important, but rather, it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover the possible Space-Occupying Lesion (PSO) in brain images and ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors.