Fractional dimension filtering for multiscale lung nodule detecton

Fei Mao; Wei Qian; Laurence P. Clarke

doi:10.1117/12.274131

25 April 1997 Fractional dimension filtering for multiscale lung nodule detecton

Fei Mao, Wei Qian, Laurence P. Clarke

Author Affiliations +

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274131
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

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.

Citation Download Citation

Fei Mao, Wei Qian, and Laurence P. Clarke "Fractional dimension filtering for multiscale lung nodule detecton", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274131

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