Fuzzy image segmentation for lung nodule detection

Yue Shen; Ravi T. Sankar; Wei Qian; Xuejun Sun; Dansheng Song

doi:10.1117/12.498263

30 December 2003 Fuzzy image segmentation for lung nodule detection

Yue Shen, Ravi T. Sankar, Wei Qian, Xuejun Sun, Dansheng Song

Proceedings Volume 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI; (2003) https://doi.org/10.1117/12.498263
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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.

Citation Download Citation

Yue Shen, Ravi T. Sankar, Wei Qian, Xuejun Sun, and Dansheng Song "Fuzzy image segmentation for lung nodule detection", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); https://doi.org/10.1117/12.498263

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