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Comparative Study
. 2013 Apr;26(2):227-38.
doi: 10.1007/s10278-012-9514-2.

Extracting fuzzy classification rules from texture segmented HRCT lung images

Manish Kakar  1 , Arianna Mencattini, Marcello Salmeri
Affiliations
Comparative Study

Extracting fuzzy classification rules from texture segmented HRCT lung images

Manish Kakar et al. J Digit Imaging. 2013 Apr.

Abstract

Automatic tools for detection and identification of lung and lesion from high-resolution CT (HRCT) are becoming increasingly important both for diagnosis and for delivering high-precision radiation therapy. However, development of robust and interpretable classifiers still presents a challenge especially in case of non-small cell lung carcinoma (NSCLC) patients. In this paper, we have attempted to devise such a classifier by extracting fuzzy rules from texture segmented regions from HRCT images of NSCLC patients. A fuzzy inference system (FIS) has been constructed starting from a feature extraction procedure applied on overlapping regions from the same organs and deriving simple if-then rules so that more linguistically interpretable decisions can be implemented. The proposed method has been tested on 138 regions extracted from CT scan images acquired from patients with lung cancer. Assuming two classes of tissues C1 (healthy tissues) and C2 (lesion) as negative and positive, respectively; preliminary results report an AUC = 0.98 for lesions and AUC = 0.93 for healthy tissue, with an optimal operating condition related to sensitivity = 0.96, and specificity = 0.98 for lesions and sensitivity 0.99, and specificity = 0.94 for healthy tissue. Finally, the following results have been obtained: false-negative rate (FNR) = 6 % (C1), FNR = 2 % (C2), false-positive rate (FPR) = 4 % (C1), FPR = 3 % (C2), true-positive rate (TPR) = 94 %, (C1) and TPR = 98 % (C2).

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Figures

Fig. 1
Fig. 1
Components of tumor tracking from image sequence
Fig. 2
Fig. 2
Examples of automatically segmented lung tissue
Fig. 3
Fig. 3
Construction of the MFs LOW and HIGH for feature solidity
Fig. 4
Fig. 4
A flowchart representing the fuzzy inference system
Fig. 5
Fig. 5
Computation of the incidence levels for feature Fk
Fig. 6
Fig. 6
The ROC curves for the FIS. The dark curve is related to lesions and the light curve to the healthy tissue. The markers locate the optimal operating points automatically identified for the two curves
None
See this image and copyright information in PMC

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