Image Dissimilarity-Based Quantification of Lung Disease from CT

In this paper, we propose to classify medical images using dissimilarities computed between collections of regions of interest. The images are mapped into a dissimilarity space using an image dissimilarity measure, and a standard vector space-based classifier is applied in this space. The classification output of this approach can be used in computer aided-diagnosis problems where the goal is to detect the presence of abnormal regions or to quantify the extent or severity of abnormalities in these regions. The proposed approach is applied to quantify chronic obstructive pulmonary disease in computed tomography (CT) images, achieving an area under the receiver operating characteristic curve of 0.817. This is significantly better compared to combining individual region classifications into an overall image classification, and compared to common computerized quantitative measures in pulmonary CT.

[1]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[2]  A. Baert,et al.  [High-resolution CT of the lung]. , 1991, Rontgenpraxis; Zeitschrift fur radiologische Technik.

[3]  Robert P. W. Duin,et al.  Dissimilarity representations allow for building good classifiers , 2002, Pattern Recognit. Lett..

[4]  Max A. Viergever,et al.  Global and Local Multi-valued Dissimilarity-Based Classification: Application to Computer-Aided Detection of Tuberculosis , 2009, MICCAI.

[5]  J. Seo,et al.  Texture-Based Quantification of Pulmonary Emphysema on High-Resolution Computed Tomography: Comparison With Density-Based Quantification and Correlation With Pulmonary Function Test , 2008, Investigative radiology.

[6]  N. Müller,et al.  "Density mask". An objective method to quantitate emphysema using computed tomography. , 1988, Chest.

[7]  Heikki Mannila,et al.  Distance measures for point sets and their computation , 1997, Acta Informatica.

[8]  Janette M. Vincent,et al.  High‐Resolution CT of the Lung, Third Edition , 2002 .

[9]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[10]  Mads Nielsen,et al.  Learning COPD Sensitive Filters in Pulmonary CT , 2009, MICCAI.

[11]  Marco Loog,et al.  Static posterior probability fusion for signal detection: applications in the detection of interstitial diseases in chest radiographs , 2004, ICPR 2004.

[12]  G. Aughenbaugh,et al.  High-Resolution CT of the Lung, 3rd ed. , 2001 .

[13]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[14]  Mads Nielsen,et al.  Automated Effect-Specific Mammographic Pattern Measures , 2008, IEEE Transactions on Medical Imaging.

[15]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[16]  Josef Kittler,et al.  Moderating k-NN Classifiers , 2002, Pattern Analysis & Applications.

[17]  Kunio Doi,et al.  Automatic detection of abnormalities in chest radiographs using local texture analysis , 2002, IEEE Transactions on Medical Imaging.

[18]  Thomas Gärtner,et al.  Multi-Instance Kernels , 2002, ICML.