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
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Scale-space texture classification using combined classifiers
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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A scale-space representation based on the Gaussian kernel filter and Gaussian derivatives filter is employed to describe HRCT lung image textures for classifying four diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing. The mean, standard deviation, skew and kurtosis along with the Haralick measures of the filtered ROIs are computed as texture features. Support vector machines (SVMs) are used to evaluate the performance of the feature extraction scheme. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512×512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs from slices already marked by experienced radiologists. We employ this technique at different scales and different directions for diffuse lung disease classification. The technique presented here has best overall sensitivity of 84.6% and specificity of 92.3%.