A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Texture Analysis in Lung HRCT Images
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Texture information in run-length matrices
IEEE Transactions on Image Processing
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Scale-space representation of lung HRCT images for diffuse lung disease classification
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
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The generalized Gaussian density model for wavelet subbands has been applied widely in texture image retrieval. In this paper, we employ wavelet-based texture extraction that is based on accurate modeling of the distribution of wavelet coefficients using generalized Gaussian density to classify four diffuse lung disease patterns: normal, emphysema, ground glass opacity and honey-combing. The evaluated classifiers are K-nearest neighbor (K-NN) and support vector machine (SVM). A collection of 124 slices from 45 patients has been investigated, each slice of size 512×512, 12bit/pixel in DICOM format. The dataset contains 6000 ROIs of those slices marked by experienced radiologists. We employ this technique at different wavelet transform scales and compare results to other wavelet-based classification techniques for diffuse lung disease classification. The technique presented here has the best overall accuracy of 92.25% for the multi-class case with 3- level wavelet transform and SVM classifier.