Corrigendum to ‘Computer-Assisted Detection of Infectious Lung Diseases: A Review’ [Computerized Medical Imaging and Graphics 36 (2012) 72–84]

Ulaş Bağcı^a, Mike Bray^b, Jesus Caban^c, Jianhua Yao^d, Daniel J. Mollura^a

^a Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USA

^b National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Bethesda, MD 20892, USA

^c National Library of Medicine, National Institutes of Health (NIH), Bethesda, MD 20892, USA

^d Department of Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, MD 20892, USA

The authors regret that incorrectly numbered references appeared in Table 4 of this article. The authors would like to apologise for any inconvenience caused. A corrected Table 4 appears below.

Study	Features	Classifier	Data	Performance or ROC / FP rates	Ref.
Interstitial Lung Disease Classification	First and second order statistics, wavelet and GLCM	SVM	CT	Az=0.831-to-0.968	[120]
Interstitial Lung Disease Classification	Multi-scale filter-banks, moments, gradient of grey level features	LDA, SVM	X-ray	Az=0.78	[87]
Interstitial Lung Disease Progression Estimation	General purpose filter-banks	LDA, k-NN, SVM	CT	Az=0.795	[121]
Interstitial Lung Disease Classification (Emphysema, GGO, honeycomb, etc.)	Adaptive Multiple Feature Method including first order statistics and GLCM	Bayes, SVM	CT	83.25% sensitivity, 97.75%specificity (for best cases)	[71]
Interstitial Lung Disease Quantification (reticular, nodular, reticulonodular, etc.)	Multi-scale filter-banks, shape features, moments, energy and local texture features.	k-NN	X-ray	Az =0.97 improved from 0.948	[28,29,41-45]
Interstitial Lung Disease Classification (Septal Lines, honeycombing, etc.)	Local texture, moments, size and energy features, homogeneity of textures, fineness and coarseness of textures	ANN	X-ray	Az =0.911 improved from 0.826	[16,122,123]