A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A family of polynomial spline wavelet transforms
Signal Processing
Wavelet-Based Texture Classification of Tissues in Computed Tomography
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
A New Segmentation Method for Lung HRCT Images
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
A System for Computer Aided Detection of Diseases Patterns in High Resolution CT Images of the Lungs
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Lung Tissue Classification in HRCT Data Integrating the Clinical Context
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Diffuse parenchymal lung diseases: 3D automated detection in MDCT
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Texture classification using rotated wavelet filters
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Isotropic polyharmonic B-splines: scaling functions and wavelets
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
| Hi-index | 12.05 |
Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, we propose a new image based system for classification of lung tissue patterns. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of overcomplete wavelet filters, namely discrete wavelet frames and rotated wavelet frames, are utilized to extract features from defined regions of interest (ROIs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. The proposed method is tested for classifying four different lung tissue patterns (ground glass, honeycombing, reticular, and normal) selected from a database of 339 images from 17 subjects. After applying our technique to classify these patterns in isolated ROIs, we extend the classification scheme to the whole lung in order to produce quantitative scores of abnormalities in lung parenchyma of patients. The performance of the proposed method is compared with two state-of-the-art texture based methods for lung tissue characterization and is also validated against experienced observers. The average kappa statistic of the agreement between two radiologists and the computer was found to be 0.6543 where as the average kappa statistic for the inter-observer agreement was 0.6848. We also performed an experiment to show the correlation between pulmonary function test parameters and quantitative scores of computerized system. Results show that extent of HRCT findings correlates significantly with functional impairment. The computer system is shown to approach the performance of the expert observers in diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT images.