ICIP'09 Proceedings of the 16th IEEE international conference 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
Texture bags: anomaly retrieval in medical images based on local 3d-texture similarity
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Automatic classification of lung tissue patterns in high resolution computed tomography images of patients with interstitial lung diseases is an important stage in the construction of a computer-aided diagnosis system. To this end, a novel approach is proposed using two sets of overcomplete wavelet filters, namely discrete wavelet frames (DWF) and rotated wavelet frames (RWF), to extract the features which best characterizes the lung tissue patterns. Support vector machines learning algorithm is then applied to perform the pattern classification. Four different lung patterns (ground glass, honey combing, reticular, and normal) selected from a database of 340 images are classified using the proposed method. The overall multiclass accuracy reaches 90.72%, 95.85%, and 96.81% for DWF, RWF, and their combination, respectively. These results prove that RWF is superior to DWF, due to its orientation selectivity. However, best results are obtained by the combination of two filter banks which shows that the two set of filters are complementary.