Supervised and unsupervised segmentation of multispectral retina images

• Autorzy: Świtoński A. , Błachowicz T. , Misiuk-Hojło M. , Wojciechowski K.

Warianty tytułu

PL

Nadzorowana i nienadzorowana segmentacja wielospektralnych obrazów dna oka

• Języki publikacji: EN

Abstrakty

EN

The segmentation method of multispectral human eye images suitable in ophthalmic diagnosis of structural retinal features characteristic for glaucoma and diabetic retinopathy diseases is presented. A multispectral imaging was realized in 21 spectral windows, between 400nm and 740nm, on a base of liquid crystal tunable filter and a high sensitivity monochrome camera. Results of supervised and unsupervised segmentation procedures of retina images, adopted from a color fundus device, are presented.

PL

W pracy zaprezentowano metodę segmentacji wielospektralnych obrazów dna oka ukierunkowaną na diagnostykę schorzeń jaskry i retinopatii cukrzycowej. Akwizycja wielospektralna prowadzona jest w 21 oknach widma z zakresu 400nm do 740nm na bazie elektronicznie sterowalnego filtra ciekłokrystalicznego i wysokiej czułości monochromatycznej kamery CCD. Przedstawiono uzyskane wyniki segmentacji w podejściu nadzorowanym i nienadzorowanym (Nadzorowana i nienadzorowana segmentacja wielospektralnych obrazów dna oka).

Słowa kluczowe

EN

multispectral imaging; supervised and unsupervised machine learning; retina diagnosis; segmentation of multichannel images

PL

obrazowanie wielospektralne; nadzorowane i nienadzorowane uczenie maszynowe; diagnostyka chorób dna oka; segmentacja obrazów wielokanałowych

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 12b
• Strony: 111--114
• Opis fizyczny: Bibliogr. 20 poz., rys.

Twórcy

autor

Świtoński A.

autor

Błachowicz T.

autor

Misiuk-Hojło M.

autor

Wojciechowski K.

• Polish-Japanese Institute of Information Technology, Faculty of Bytom,

Bibliografia

• [1] Świtoński A., Michalak M., Josinski H., Wojciechowski K., "Detection of tumor tissue based on the multispectral imaging" in Computer Vision and Graphics. Lecture Notes in Computer Science, R. Bieda Ed. (2010), pp. 325-333.
• [2] Kruse F. A., "Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada and California," Remote Sensing Environment 24, (1988), p. 31-51
• [3] Lobo L. C., Ersoy O. K., Miles G. E., Multispectral Imaging, Image-Processing and Classification for Agriculture, Purdue University, Purdue e-Pubs, (2000).
• [4] Imai F. H., Rosen M. R., Berns R. S., "Multi-spectral imaging of van Gogh's self-portrait at the National Gallery of Art, Washington, D.C.," in Proceedings of Image Processing, Image Quality, Image Capturing System Conference (The Society for Imaging Science and Technology, 2001) pp. 185-189.
• [5] Liu X., Wang D., Liu, F. Bai J., "Principal component analysis of dynamic fluorescence diffuse optical tomography images," Opt. Express 18, (2010), p. 6300-6314.
• [6] Zhang X. Xu H., "Reconstructing spectral reflectance by dividing spectral space and extending the principal components in principal component analysis," J. Opt. Soc. Am. A 25, (2008), p. 371-378.
• [7] Kim Y., "Incremental principal component analysis for image processing," Opt. Lett. 32, (2007), p 32-34.
• [8] Schölkopf B. Smola A., Learning with Kernels. Support Vector Machines, Regularization, Optimization, and Beyond (Massachusetts Institute of Technology, 2002).
• [9] Schelkanova I., Toronov, V. " Independent component analysis of broadband near-infrared spectroscopy data acquired on adult human head," Biom. Opt. Express 3, (2011), p. 64-74.
• [10] Kuan C.-Y. Healey G., "Using independent component analysis for material estimation in hyperspectral images," J. Opt. Soc. Am. A 21, (2008), p. 1026-1034.
• [11] Vermeer K.A., van der Schoot J.,Lemij H. G., de Boer J. F., "Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images," Biom. Opt. Express 2, (2011), p. 1743-1756.
• [12] Chiu S. J., Toth C. A., Rickman C. B., Izatt J. A., Farsiu S., "Automatic segmentation of Closed-contour features in segmentation of Closed-contour features in ophthalmic images using graph theory and dynamic programming," Biomed. Opt. Express 3, (2012), p. 1127-1140.
• [13] Yazdanpanah A., Hamarneh G., "Segmentation of intraretinal layers from optical coherence tomography images using an active contour approach," IEEE Trans. Med. Imag. 30, (2011), p. 484-496.
• [14] Liu Y.-Y., Chen M., Ishikawa H., Wollstein G., Schuman J.S., Rehg J. M., "Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding," Med. Image Anal. 15, (2011), p. 748-759.
• [15] Kokaram A.C., Persad N., Lasenby J., Fitzgerald W. J., McKinnon A., Welland M., "Restoration of images from the scanning-tunneling microscope," Appl. Opt. 34, (1995), p. 5121-5132.
• [16] Świtoński A., Blachowicz T., Zieliński M., Josiński H., "Dimensionality reduction of multispectral images representing anatomical structures of an eye," in Proceedings of the International MultiConference of Engineers and Computer Scientists (IMECS 2012, March 14-16, Hong Kong), p. 740-745.
• [17] Witten I. Frank E., Data Mining: Practical Machine Learning. Tools and Techniques (Morgan Kaufmann Publishers, San Francisco, 2005).
• [18] Świtonski A., Josinski H., Jedrasiak K., Polanski A.,. Wojciechowski K., "Classification of poses and movement phases," in Proceedings of International Conference of Computer Vision and Graphics (Lecture Notes in Computer Science, Springer, 2010), p. 193-200.
• [19] Michalak M. Switonski A., "Kernel Postprocessing of Multispectral Images Computer Recognition Systems," Comp. Reg. Sys. 4, (2011), p 395-401.
• [20] The Waikato Environment for Knowledge Analysis (WEKA) located at the University of Wakaito (New Zeland); http://www.cs.waikato.ac.nz/ml/weka/.