This work focuses on image retrieval utilizing principal component analysis (PCA) and linear discriminant analysis (LDA) techniques for brain tumors from Magnetic Resonance (MR) studies. The research has been broken into three stages. Stage 1 consists of developing the PCA and LDA algorithms to be used for content based image retrieval (CBIR) systems. Stage 2 consists of evaluation of PCA and LDA algorithms on synthetic tumor images with added noise and shading artifacts. Stage 3 consists of tailoring the algorithm specifically for automated detection and CBIR system of MR contrast enhancing tumors matching a given query image. The algorithm has been developed and tested successfully for synthetic tumor images and actual contrast enhanced tumors. We hope to integrate the PCA and LDA algorithms to perform an indexing of the tumor shapes derived from actual MR images. Two relevant indices: size and location will also be used to index the data.
The decrease in reimbursement rates for radiology procedures has placed even more pressure on radiology departments
to increase their clinical productivity. Clinical faculties have less time for teaching residents, but with the advent and
prevalence of an electronic environment that includes PACS, RIS, and HIS, there is an opportunity to create electronic
teaching files for fellows, residents, and medical students. Experienced clinicians, who select the most appropriate
radiographic image, and clinical information relevant to that patient, create these teaching files. Important cases are
selected based on the difficulty in determining the diagnosis or the manifestation of rare diseases. This manual process of
teaching file creation is time consuming and may not be practical under the pressure of increased demands on the
radiologist. It is the goal of this research to automate the process of teaching file creation by manually selecting key
images and automatically extracting key sections from clinical reports and laboratories. The text report is then processed
for indexing to two standard nomenclatures UMLS and RADLEX. Interesting teaching files can then be queried based
on specific anatomy and findings found within the clinical reports.
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