Análisis de imagen retiniana: Procesado de imagen y extracción de características orientado a la tarea clínica
A.G. Marrugo, M. S. Millán
Descargar artículo
Información básica
Volumen
V50 - N1 / 2017 Ordinario
Referencia
49-62
DOI
http://doi.org/10.7149/OPA.50.1.49507
Idioma
English
Etiquetas
Diagnóstico asistido por computador, imagen médica, imagen retiniana, telemedicina, oftalmología
Resumen
La captura de imagen digital es una parte fundamental de los procedimientos médicos modernos. Provee de documentación visual, un registro permanente para los pacientes y la posibilidad de extraer información cuantitativa sobre muchas enfermedades. La oftalmología depende considerablemente del análisis digital de imágenes. En este trabajo se presentan los resultados principales de la tesis doctoral de Andrés G. Marrugo. Este trabajo contribuye al análisis digital de tales imágenes y los problemas que surgen a lo largo del proceso de formación de imagen. Este campo se le conoce como análisis de imagen retiniana. En esta tesis se han propuesto soluciones a problemas asociados con la adquisición de imagen retiniana y la detección de cambios temporales en patologías retinianas. Específicamente, los problemas de iluminación no-uniforme, baja calidad de imagen, enfoque automático, y análisis multi-canal. Sin embargo, existen situaciones inevitables en que se adquieren imágenes de baja calidad, como imágenes emborronadas debido a las aberraciones del ojo. Este problema lo hemos abordado utilizando dos metodologías para la deconvolución ciega de imágenes. En la primera aproximación, consideramos que el emborronamiento es invariante espacialmente y en la segunda aproximación extendimos el trabajo y propusimos un método más general espacialmente variante.
Referencias
M. D. Abramoff, M. Garvin, and M. Sonka, "Retinal Imaging and Image Analysis," Biomedical Engineering, IEEE Reviews 3, 169–208, (2010). DOI
A. G. Marrugo, "Comprehensive Retinal Image Analysis: Image Processing and Feature Extraction Techniques Oriented to the Clinical Task.," Universitat Politècnica de Catalunya, Barcelona, (2013). http://hdl.handle.net/10803/134698
A. G. Marrugo, M. S. Millan, G. Cristóbal, S. Gabarda, and H. C. Abril, "No-reference Quality Metrics for Eye Fundus Imaging," CAIP 2011, LNCS, 6854, 486–493, (2011).
S. Gabarda and G. Cristóbal, "Blind image quality assessment through anisotropy," J Opt Soc Am A Opt Image Sci Vis, 24 , B42–51, (2007). DOI
Xiang Zhu and P. Milanfar, "Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content," IEEE Trans Image Process. 19, 3116–3132, (2010). DOI
R. Ferzli and L. J. Karam, "A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)," IEEE Trans Image Process. 18, 717–728, (2009). DOI
Y. Qu, Z. Pu, H. Zhao, and Y. Zhao, "Comparison of different quality assessment functions in autoregulative illumination intensity algorithms," Opt. Eng. 45, 117201, (2006). DOI
A. G. Marrugo, M. S. Millan, G. Cristóbal, S. Gabarda, and H. C. Abril, "Anisotropy-based robust focus measure for non-mydriatic retinal imaging," J. Biomed. Opt., 17, 076021, (2012). DOI
M. Moscaritolo, H. Jampel, F. Knezevich, and R. Zeimer, "An Image Based Auto-Focusing Algorithm for Digital Fundus Photography," IEEE Trans. Med. Imaging, 28, 1703–1707, (2009). DOI
S. Nayar and Y. Nakagawa, "Shape from focus," IEEE Trans Pattern Anal Mach Intell, 16, 824–831, (1994). DOI
K. Choi, J. Lee, and S. Ko, "New autofocusing technique using the frequency selective weighted median filter for video cameras," IEEE T. Consum. Electr., 45, 820–827, (1999). DOI
M. Kristan, J. Pers, M. Perse, and S. Kovacic, "A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform," Pattern Recognit. Lett., 27, 1431–1439, (2006). DOI
A. G. Marrugo, M. S. Millan, and H. C. Abril, "Implementation of an image based focusing algorithm for non-mydriatic retinal imaging," Engineering Mechatronics and Automation (CIIMA), 2014 III International Congress of, 1–3, (2014). DOI
A. G. Marrugo, Ed., Anisotropy focus measure. [Online Accessed: 17-Apr-2016]. Available at: Anisotropy focus
H. Narasimha-Iyer, A. Can, B. Roysam, C. Stewart, H. Tanenbaum, A. Majerovics, and H. Singh, "Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy," IEEE Trans. Biomed. Eng., 53, 1084–1098, (2005) DOI
A. G. Marrugo, F. Sroubek, M. Sorel, and M. S. Millan, "Multichannel blind deconvolution in eye fundus imaging," presented at the ISABEL '11-Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, 7:1–5 (2011). DOI
N. Everdell, I. Styles, A. Calcagni, J. Gibson, J. Hebden, and E. Claridge, "Multispectral imaging of the ocular fundus using light emitting diode illumination," Rev. Sci. Instrum., 81, 093706–093709, (2010). DOI
A. G. Marrugo, M. Sorel, F. Sroubek, and M. S. Millan, "Retinal image restoration by means of blind deconvolution," J. Biomed. Opt., 16, 11, 116016, (2011). DOI
C. Stewart, Chia-Ling Tsai, and B. Roysam, "The dual-bootstrap iterative closest point algorithm with application to retinal image registration," IEEE Trans. Med. Imaging, 22, 1379–1394, (2003).
T. Aach and A. Kaup, "Bayesian Algorithms for Change Detection in Image Sequences Using Markov Random Fields," Signal Process. Image 7, 147–160, (1995) DOI
A. Levin, Y. Weiss, F. Durand, and W. Freeman, "Understanding Blind Deconvolution Algorithms," IEEE Trans Pattern Anal. Mach Intell., 12, 2354–2367, (2011). DOI
A. G. Marrugo, M. S. Millan, G. Cristóbal, S. Gabarda, M. Sorel, and F. Sroubek, "Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine," presented at the Proceedings SPIE, 8436, 84360C–84360C–10 (2012). DOI
C. Muramatsu, Y. Hayashi, A. Sawada, Y. Hatanaka, T. Hara, T. Yamamoto, and H. Fujita, "Detection of retinal nerve fiber layer defects on retinal fundus images for early diagnosis of glaucoma," J. Biomed. Opt., 15, 016021, (2010). DOI
L. Xu and S. Luo, "Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve," J. Biomed. Opt., 15, 065004, (2010). DOI
P. Bedggood, M. Daaboul, R. Ashman, G. Smith, and A. Metha, "Characteristics of the human isoplanatic patch and implications for adaptive optics retinal imaging," J. Biomed. Opt., 13, 024008, (2008). DOI
A. G. Marrugo, M. S. Millan, M. Sorel, and F. Sroubek, "Restoration of retinal images with space-variant blur," J. Biomed. Opt., 19, no. 1, p. 016023, Jan. 2014. DOI
A. G. Marrugo, M. S. Millan, M. Sorel, J. Kotera, and F. Sroubek, "Improving the blind restoration of retinal images by means of point-spread-function estimation assessment," presented at the Tenth International Symposium on Medical Information Processing and Analysis, 9287, 92871D (2015).
M. Tallón, J. Mateos, S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Space-variant blur deconvolution and denoising in the dual exposure problem," INFORMATION FUSION, (2012).
A. G. Marrugo, M. S. Millan, M. Sorel, and F. Sroubek, "Blind restoration of retinal images degraded by space-variant blur with adaptive blur estimation," presented at the 8th Ibero American Optics Meeting/11th Latin American Meeting on Optics, Lasers, and Applications 8785, 8785D1 (2013)