An algorithm is developed that detects well-localized, unfragmented, thin edges in medical images based on optimization of edge configurations using a genetic algorithm (GA). Several enhancements were added to improve the performance of the algorithm over a traditional GA. The edge map is split into connected subregions to reduce the solution space and simplify the problem. The edge-map is then optimized in parallel using incorporated genetic operators that perform transforms on edge structures. Adaptation is used to control operator probabilities based on their participation. The GA was compared to the simulated annealing (SA) approach using ideal and actual medical images from different modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. Quantitative comparisons were provided based on the Pratt figure of merit and on the cost-function minimization. The detected edges were thin, continuous, and well localized. Most of the basic edge features were detected. Results for different medical image modalities are promising and encourage further investigation to improve the accuracy and experiment with different cost functions and genetic operators.

A framework for retrieving images by spatial similarity (FRISS) in image databases is presented. In this framework, a robust retrieval by spatial similarity (RSS) algorithm is defined as one that incorporates both directional and topological spatial constraints, retrieves similar images, and recognizes images even after they undergo translation, scaling, rotation (both perfect and multiple), or any arbitrary combination of transformations. The FRISS framework is discussed and used as a base for comparing various existing RSS algorithms. Analysis shows that none of them satisfies all the FRISS specifications. An algorithm, SIM DTC , is then presented. SIM DTC introduces the concept of a rotation correction angle (RCA) to align objects in one image spatially closer to matching objects in another image for more accurate similarity assessment. Similarity between two images is a function of the number of common objects between them and the closeness of directional and topological spatial relationships between object pairs in both images. The SIM DTC retrieval is invariant under translation, scaling, and perfect rotation, and the algorithm is able to rank multiple rotation variants. The algorithm was tested using synthetic images and the TESSA image database. Analysis shows the robustness of the SIM DTC algorithm over current algorithms.

A COntent-Based Retrieval Architecture [COBRA) for picture archiving and communication systems (PACS) is introduced. COBRA improves the diagnosis, research, and training capabilities of PACS systems by adding retrieval by content features to those systems.COBRA is an open architecture based on widely used health care and technology standards. In addition to regular PACS components, COBRA includes additional components to handle representation, storage, and content-based similarity retrieval. Within COBRA, an anatomy ciassification algorithm is introduced to automatically classify PACS studies based on their anatomy. Such a classification allows the use of different segmentation and image-processing algorithms for different anatomies. COBRA uses primitive retrieval criteria such as color, texture, shape, and more complex criteria including object-based spatial relations and regions of interest. A prototype content-based retrieval system for MR brain images was developed to illustrate the concepts introduced in COBRA. Copyright 9 2000 by W.B. Saunders CompanyKEY WORDS: content-based image retrieval, medical image databases, medical information system, picture archiving and communication systems, information retrieval.

Large image databases have emerged in various applications in recent years. A prime requisite of these databases is the means by which their contents can be indexed and retrieved. A multilevel signature file called the Two Signature Multi-Level Signature File (2SMLSF) is introduced as an efficient access structure for large image databases. The 2SMLSF encodes image information into binary signatures and creates a tree structure that can be efficiently searched to satisfy a user's query. Two types of signatures are generated. Type I signatures are used at all tree levels except the leaf level and are based only on the domain objects included in the image. Type II signatures, on the other hand, are stored at the leaf level and are based on the included domain objects and their spatial relationships. The 2SMLSF was compared analytically to existing signature file techniques. The 2SMLSF significantly reduces the storage requirements; the index structure can answer more queries; and the 2SMLSF performance significantly improves over current techniques. Both storage reduction and performance improvement increase with the number of objects per image and the number of images in the database. For an example large image databases, a storage reduction of 78% may be achieved while the performance improvement may reach 98%.