Retrieval and classification of pneumoconiosis chest radiograph images using multiscale AM-FM methods

We propose the use of Amplitude-Modulation Frequency-Modulation (AM-FM) features for representing and retrieving X-Ray images with pneumoconiosis. The AM-FM features are estimated using multiscale filterbanks with wave-lengths related with the standard sizes for grading the level of opacities in X-Rays. The extracted AM-FM features represent opacity profusion in terms of instantaneous frequency (IF) and instantaneous amplitude (IA) features. Here, IF estimates in the medium and high scale frequencies can be used to capture early disease symptoms. AM-FM features from the low and medium scale frequencies are associated with advanced disease stages. We demonstrate the performance of the system in X-ray image retrieval and classification applications.

[1]  Hiroshi Kondo,et al.  Computer-Aided Diagnosis for Pnemoconiosis Using Neural Network , 2001 .

[2]  Joseph P. Havlicek,et al.  AM-FM Image Models: Fundamental Techniques and Emerging Trends , 2005 .

[3]  Jun-ichi Hasegawa,et al.  Intelligent retrieval of chest X-ray image database using sketches , 1989, Systems and Computers in Japan.

[4]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[5]  P. Soliz,et al.  New AM-FM analysis methods for retinal image characterization , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[6]  Marios S. Pattichis,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Analyzing Image Structure by Multidimensional Frequency Modulation Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2006 .

[7]  T M Lehmann,et al.  Content-based Image Retrieval in Medical Applications , 2004, Methods of Information in Medicine.

[8]  H. Kondo,et al.  Detection of pneumoconiosis rounded opacities using neural network , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[9]  P. Elmes,et al.  International classification of radiographs of pneumoconioses. , 1971, British journal of industrial medicine.

[10]  Marios S. Pattichis,et al.  Optimal scanning, display, and segmentation of the International Labor Organization (ILO) X-ray images set for pneumoconiosis , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[11]  Marios S. Pattichis,et al.  A screening system for the assessment of opacity profusion in chest radiographs of miners with pneumoconiosis , 2002, Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation.

[12]  Victor Manuel,et al.  AM-FM methods for image and video processing , 2009 .

[13]  Alan C. Bovik,et al.  Am-fm image models , 1996 .

[14]  Hiroshi Kondo,et al.  Computer-aided diagnosis for pneumoconiosis using neural network , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[15]  Marios S. Pattichis,et al.  New image processing models for opacity image analysis in chest radiographs , 2002, Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation.

[16]  Marios S. Pattichis,et al.  Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy , 2010, IEEE Transactions on Image Processing.

[17]  Marios S. Pattichis,et al.  Multiscale AM-FM analysis of pneumoconiosis x-ray images , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).