## 1 Introduction  
In the clinical practice of reading and interpreting medical images, clinicians (i.e., radiologists) often refer to and compare the similar cases with verified diagnostic results in their decision making of detecting and diagnosing suspicious lesions or diseases. However, searching for and identifying the similar reference cases (or images) from the large and diverse clinical databases (either the conventional film/paper based libraries or advanced digital image storage systems) is a quite difficult task. The advance in digital technologies for computing, networking, and database storage has enabled the automated searching for clinically relevant and visually similar medical examinations (cases) to the queried case from the large image databases. There are two types of general approaches in medical image retrieval namely, the text (or semantic) based image retrieval (TBIR) and the content-based image retrieval (CBIR). Currently, the most of available search systems (or tools) developed and implemented in medical informatics and picture archiving and communication systems (PACS) use TBIR schemes that are based on the annotated textual information to select similar or clinically relevant references (cases) [1-4]. This approach is typically limited to retrieve or select the same type of medical images (i.e., mammograms or CT brain images). However, the relevant clinical information depicted on medical images is locally presented (i.e., breast masses depicted on mammograms and emphysema lesions depicted on lung CT images). In the clinical practice of reading and interpreting medical images, the nature of the queried suspicious regions is often un-determined. Thus, the CBIR is the only available and reliable approach to retrieve the clinically relevant (reference) cases along with the proven pathology and other related clinical information. As a result, developing CBIR schemes has been attracting extensive research interest in the areas of medical informatics and PACS for the last decade [5-10]. Despite the fact that CBIR approach is still in its early development stage facing many technical challenges (i.e., region segmentation, semantic gap, and computational efficiency), as the digital medical images are produced in ever increasing quantities and used for diagnosis and therapy, the researchers believe that the advance of CBIR development will play more and more important role in future medical image diagnosis and patient treatment (or management) [11]. In medical imaging areas, developing computer-aided detection and/or diagnosis (CAD) schemes has also been a very active research topic for the last two decades. The CAD schemes have been developed for variety of medical images including but not limited to mammograms (i.e., detecting breast masses and micro-calcification clusters [12]), lung CT images (i.e., detecting lung nodules [13], interstitial lung diseases [14], chronic obstructive pulmonary disease [15], and pulmonary embolism [16]), CT colonography [17], and pathology images (i.e., fluorescent in situ hybridization (FISH) images for diagnosing breast cancer [18] and metaphase chromosome images for detecting leukemia [19]). Among these different types of CAD schemes, CAD of mammograms is the most mature one. The commercialized CAD systems of mammograms have been routinely used in the clinical practice in a large number of medical institutions in USA and other countries to assist radiologists in detecting breast abnormalities (by highlighting or cueing the locations depicting the suspicious micro-calcification clusters and masses) [12]. Previous studies have shown that using CAD improves radiologists' efficiency in searching for and detecting micro-calcification clusters as well as helps them detect more cancers associated with malignant micro-calcifications [20, 21].