The reliance of life science researchers on computer systems has increased dramatically with the progress of the various genome projects. Currently, the demand exists for a biological image database system for the storage and retrieval of RFLP (Restriction Fragment Length Polymorphism) images. To fulfill this need, we have developed a content-based image retrieval (CBIR) system - RFLPRetriever - for the biological research community. Implementing a CBIR system for storage and retrieval of RFLP images provides several advantages. For example, when a biologist clones a gene, he or she must discover whether the gene has previously been cloned. To do this, the researcher needs to look through thousands of images in search of an RFLP image that contains a similar set of biologically meaningful features as a related image created in the lab. One advantage of using a CBIR system is that by taking the extracted feature vectors, an efficient database indexing structure can be created, providing the system with a rapid retrieval rate. In addition, a CBIR system allows the user to search an image database by submitting a query image in which the user wants to find a matching RFLP image based on a combination of biologically relevant attributes contained in the query image.
This paper introduces a new approach called the 'customized- queries' approach to content-based image retrieval (CBIR). The customized-queries approach first classifies a query using the features that best differentiate the major classes and then customizes the query to that class by using the features that best distinguish the subclasses within the chosen major class. This research is motivated by the observation that the features which are most effective in discriminating among images from different classes may not be the most effective for retrieval of visually similar images within a class. This occurs for domains in which not all pairs of images within one class have equivalent visual similarity. We apply this approach to content-based retrieval of high-resolution tomographic images of patients with lung disease and show that this approach yields 82.8 percent retrieval precision. The traditional approach that performs retrieval using a single feature vector yields only 37.9 percent retrieval precision.
In the picture archiving and communication systems (PACS) used in modern hospitals, the current practice is to retrieve images based on keyword search, which returns a complete set of images from the same scan. Both diagnostically useful and negligible images in the image databases are retrieved and browsed by the physicians. In addition to the text-based search query method, queries based on image contents and image examples have been developed and integrated into existing PACS systems. Most of the content-based image retrieval (CBIR) systems for medical image databases are designed to retrieve images individually. However, in a database of tomographic images, it is often diagnostically more useful to simultaneously retrieve multiple images that are closely related for various reasons, such as physiological continguousness, etc. For example, high resolution computed tomography (HRCT) images are taken in a series of cross-sectional slices of human body. Typically, several slices are relevant for making a diagnosis, requiring a PACS system that can retrieve a contiguous sequence of slices. In this paper, we present an extension to our physician-in-the-loop CBIR system, which allows our algorithms to automatically determine the number of adjoining images to retain after certain key images are identified by the physician. Only the key images, so identified by the physician, and the other adjoining images that cohere with the key images are kept on-line for fast retrieval; the rest of the images can be discarded if so desired. This results in large reduction in the amount of storage needed for fast retrieval.
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