The paper deals with an image database organization and utilization in computer-aided cytology. To illustrate the idea we take as an example the problem of bladder cancer early detection based on urine cytology. In spite of its diagnostic potential for discovering malignancy associated changes (MAC) at the cell level it seems to be underestimated. There is common view that sensitivity of the method, especially for early cancer stages, is relatively low. We depict here just one but significant direction of our works that aims to support pathologists making the diagnosis more accurate and reliable. The key idea relies on automatic searching for MAC by comparing nuclear chromatin structure of objects in a smear with a collection of sample patterns contained in a pathomorphological image database.
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The paper deals with the problem of early detection of bladder cancer based on non-invasive, voided urine cytological investigations. In spite of the diagnostic potential of the method for discovering malignancy associated changes in cells before they start to form a tumour, cytological tests seem to be underestimated by physicians, as there is a common view that their sensitivity, especially in early stages of the cancer, is relatively low. We depict here just one, but significant, direction of our work that aims to support the cytopathologist in making the diagnosis more accurate and reliable. The key idea relies on classification of adaptive smear objects by searching for similar patterns in a flexible pathomorphological image database using content-based image retrieval technology (CBIR).
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