Fractional dimension filtering for multiscale lung nodule detecton
Fei Mao, Wei Qian, Laurence P. Clarke · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
Lung nodule (LN) detection using computer assisted diagnostic (CAD) methodology in chest radiolographs is generally composed of two steps, i.e., suspicious area (SA) location and differentiation of 'true' nodules from 'false' nodules among located SAs. The first step is related to computer image processing techniques, such as image enhancement and segmentation methods. The second step uses pattern classification techniques, such as statistical classifiers and artificial neural networks (ANN). This paper will address only the first step of the CAD lung nodule detection. We have designed a novel fractional dimension filtering (FDF) algorithm for the extraction of lung nodule patterns, which generally appear as circular bright areas in the chest radiograph. The FDF provides an improved performance of discriminating circular pattern from other patterns in the presence of overlapping structures. A multiscale analysis has also been introduced to locate multiscale nodules and eliminate false positives. A computed ROC analysis has been performed to show the improvement of discriminating performance of the FDF by using simulated patterns. A computed FROC analysis has also been conducted for analyzing the performance of the proposed location scheme with and without the multiscale analysis.