Fuzzy image segmentation for lung nodule detection

Abstract

This paper focuses on evaluating three fuzzy image segmentation algorithms in lung nodule detection scenario: fuzzy entropy-based method, multivariate fuzzy C-means method (MFCM), adaptive fuzzy C-means method (AFCM) and comparing them with the iterative threshold selection method. The experimental result shows that all three methods outperform iterative threshold selection method. The two fuzzy C-means clustering based algorithms achieve better segmentation performance without losing true positives. However, fuzzy entropy-based image segmentation removes the false positives at the cost of losing some true positives, which is a risky approach and hence it is not recommended for lung nodule detection. Moreover, although AFCM outperforms MFCM in true positive detection significantly, in the sense of TPR/FP, MFCM is comparable to AFCM in the confidence interval of significant level 0.95, since AFCM brings in more false positives than MFCM.