Use Of Random Process-Based Fractal Measure For Characterization Nodules And Suspicious Regions In Lung

Chest X-ray (CXR) data is a projection image where each pixel of it represents a volumetric integration. Consequently identification of nodules and their characteristics is a difficult task in such images. Using a novel application of random process-based fractal image processing technique we extract features for nodule characterization. The uniqueness of the proposed technique lies in the fact that instead of relying on apriori information from user as in other random process inspired measures, we translate the random walk process into a feature which is based on its realization values. The Normalized Fractional Brownian Motion (NFBM) Model is derived from the random walk process.Using neighborhood region information in an incremental manner we can characterize the smoothness or roughness of a surface. The NFBM system gives a measure of roughness of a surface which in our case is a suspicious region (probable nodule). A classification procedure uses this measure to categorize nodule and non-nodule structures in the lung.The NFBM feature set is integrated in a prototype CAD system for nodule detection in CXR. Our algorithm provided a sensitivity of 75.9% with 3.1 FP/image on an independent test set of 50 CXR studies.