A content-based image retrieval framework for multi-modality lung images

Proceedings - IEEE Symposium on Computer-Based Medical Systems

Volumn , Issue , 2010, Pages 285-290

  Song, Yang ^a   Cai, Weidong ^a   Eberl, Stefan ^a,b   Fulham, Michael J ^a,b,c   Feng, Dagan ^a,d  

^a UNIVERSITY OF SYDNEY   (Australia)

^b ROYAL PRINCE ALFRED HOSPITAL   (Australia)

^c UNIVERSITY OF SYDNEY   (Australia)

^d HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL DATA; CONTENT BASED IMAGE RETRIEVAL; LUNG CANCER; LUNG FIELDS; MULTI-MODALITY; RETRIEVAL PERFORMANCE; SIMILARITY MEASURE; TEXTURE FEATURE EXTRACTION; TUMOR STAGING;

BIOLOGICAL ORGANS; CONTENT BASED RETRIEVAL; FEATURE EXTRACTION; MEDICAL IMAGING; SEARCH ENGINES;

COMPUTERIZED TOMOGRAPHY;

EID: 80055087860     PISSN: 10637125     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CBMS.2010.6042657     Document Type: Conference Paper

References (20)

1. S. G. Armato and W. F. Sensakovic. Automated lung segmentation for thoracic CT. Academic Radiology, 11(9):1011-1021, 2004.
2. Y. Arzhaeva, M. Prokop, K. Murphy, and E. M. Rikxoort. Automated estimation of progression of interstitial lung disease in CT images. Medical Physics, 37(1):63-73, 2010.
3. P. H. Bugatti, M. P. Silva, A. J. M. Traina, C. Traina, and P. M. A. Marques. Content-based retrieval of medical images: from context to perception. Proceedings CBMS, 2009.
4. L. Chen, G. Lu, and D. Zhang. Effects of different Gabor filters parameters on image retrieval by texture. Proceedings International Multimedia Modelling Conference, pages 273-278, 2004.
5. I. ElNaqa, P. W. Grigsby, A. Apte, E. Kidd, E. Donnelly, D. Khullar, S. Chaudhari, D. Yang, M. Schmitt, R. Laforest, W. L. Thorstad, and J. O. Deasy. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognition, 42(6):1162-1171, 2009.
6. H. Greenspan and T. M. Deserno. Content-based image retrieval for biomedical image archives: achievements, problems, and prospects. Proceedings MICCAI 2007 Workshop, pages 1-6, 2007.
7. H. Guan, S. Antani, L. R. Long, and G. R. Thoma. Minimizing the semantic gap in biomedical content-based image retrieval. Proceedings SPIE, pages 45-52, 2010.
8. S. Hu, E. Hoffman, and J. Reinhardt. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging, 20:490-498, 2001.
9. Y. Huan, C. Caldwell, K. Mah, and D. Mozeg. Coregistered-FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging, 28(3):374-383, 2009.
10. M. Kakar and D. R. Olsen. Automatic segmentation and recognition of lungs and lesion from CT scans of thorax. Computerized Medical Imaging and Graphics, 33(1):72-82, 2009.
11. J. Kim, L. Constantinescu, W. Cai, and D. Feng. Contentbased dual-modality biomedical data retrieval using co-aligned functional and anatomical features. Proceedings MICCAI 2007 Workshop, pages 45-52, 2007.
12. P. Korfiatis, C. Kalogeropoulou, A. Karahaliou, and A. Kazantzi. Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Medical Physics, 35(12):5290-5302, 2008.
13. H. Muller, N. Michoux, D. Bandon, and A. Geissbuhler. A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. International Journal of Medical Informatics, 73:1-23, 2004.
14. N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. Sys., Man., Cyber, 9:62-66, 1979.
15. M. M. Rahman, S. K. Antani, and G. R. Thoma. A medical image retrieval framework in correlation enhanced visual concept feature space. Proceedings CBMS, 2009.
16. M. M. Rahman, P. Bhattacharya, and B. C. Desai. A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. IEEE Transactions on Information Technology in Biomedicine, 11(1):58-69, 2007.
17. P. J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20:53-65, 1987.
18. G. V. Saradhi, G. Gopalakrishnan, A. S. Roy, R. Mullick, R. Manjeshwar, K. Thielemans, and U. Patil. A framework for automated tumor detection in thoracic FDG PET images using texture-based features. Proceedings ISBI, pages 97-100, 2009.
19. C. R. Shyu, C. E. Brodley, A. C. Kak, and A. Kosaka. ASSERT: A physician-in-the-loop content-based retrieval system for HRCT image databases. Computer Vision and Image Understanding, 75(1/2):111-132, 1999.
20. W. Webb, N. Muller, and D. Naidich. High resolution CT of the lung. Lippincott Williams Wilkins, Philadelphia, PA, 2001.