Estimating local scaling properties for the classification of interstitial lung disease patterns

Markus B. Huber; Mahesh B. Nagarajan; Gerda Leinsinger; Lawrence A. Ray; Axel Wismueller

doi:10.1117/12.878253

8 March 2011 Estimating local scaling properties for the classification of interstitial lung disease patterns

Markus B. Huber, Mahesh B. Nagarajan, Gerda Leinsinger, Lawrence A. Ray, Axel Wismueller

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79631Q (2011) https://doi.org/10.1117/12.878253
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Local scaling properties of texture regions were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honeycombing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and the estimation of local scaling properties with Scaling Index Method (SIM). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions including the Bonferroni correction. The best classification results were obtained by the set of SIM features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers with the highest accuracy (94.1%, 93.7%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced texture features using local scaling properties can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.

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Markus B. Huber, Mahesh B. Nagarajan, Gerda Leinsinger, Lawrence A. Ray, and Axel Wismueller "Estimating local scaling properties for the classification of interstitial lung disease patterns", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79631Q (8 March 2011); https://doi.org/10.1117/12.878253

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KEYWORDS

Lung

Image classification

Tissues

Computed tomography

Chest

Computer aided diagnosis and therapy

CT reconstruction

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