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Content Based Image Retrieval for Decision Support

The number of images in Radiology is exploding. Diagnostic radiologists are confronted with the challenge of efficiently and accurately interpreting cross sectional imaging exams that often now contain thousands of images per patient study. Currently, this is largely an unassisted process, and a given reader's accuracy is established through training and experience. There is significant variation in interpretation between radiologists, and accuracy varies widely, a problem compounded by increasing image numbers. There is an opportunity to improve diagnostic decision making by enabling radiologists to search databases of radiological images and reports for cases that are similar in terms of shared imaging features to the images they are interpreting.

We are creating software tools that can be used to create and to search databases of radiological images based on image features, which include detailed information about lesions: (1) feature descriptors coded by radiologists using RadLex, a comprehensive controlled terminology, and (2) computer-generated features of pixels characterizing the lesion's interior texture and the sharpness of its boundary.

Our goal is to develop methods to facilitate the retrieval of radiological images that contain similarly appearing lesions. We are currently developing a CBIR system in CT images of the liver.