Dr. Rodrigo Nava Profile

Dr. Rodrigo Nava

Junior R&T Associate

SPIE Involvement:

Author

Area of Expertise:

Image Processing , Wavelets , Image Fusion , Image Quality Assessment , Texture Analysis , Medical imaging

Rodrigo Nava received the B.Eng., M.Eng., and D.Eng. degrees (Hons.) from Universidad Nacional Autónoma de México. He was a Postdoctoral Fellow with Czech Technical University in Prague, from 2014 to 2017. He is currently a Junior Research and Technology Associate with the Luxembourg Institute of Science and Technology. He is also a member of the National System of Researchers of the National Council of Science and Technology in Mexico: Level C (2015-2017), Level 1 (2018-2024).

His current research interests include bio-inspired image models, image quality, textures, medical imaging, microscopy image applications, and industrial imaging solutions.

Proceedings Article | 29 April 2016 Paper

Texel-based image classification with orthogonal bases

Erik Carbajal-Degante, Rodrigo Nava, Jimena Olveres, Boris Escalante-Ramírez, Jan Kybic

Proceedings Volume 9896, 98961K (2016) https://doi.org/10.1117/12.2228694

KEYWORDS: Image classification, Image classification, Image analysis, Windows, Visualization, Signal processing, Computer science, Image retrieval, Databases, Error analysis, Matrices, Statistical analysis, Fourier transforms, Model-based design, Statistical modeling

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Proceedings Article | 15 May 2014 Paper

Texture descriptor approaches to level set segmentation in medical images

Jimena Olveres, Rodrigo Nava, Ernesto Moya-Albor, Boris Escalante-Ramírez, Jorge Brieva, Gabriel Cristóbal, Enrique Vallejo

Proceedings Volume 9138, 91380J (2014) https://doi.org/10.1117/12.2054527

KEYWORDS: Image segmentation, Blood, Medical imaging, Image processing algorithms and systems, Binary data, Signal attenuation, Computed tomography, Heart, Medical diagnostics, Image understanding

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Proceedings Article | 19 November 2013 Paper

A probabilistic model of emphysema based on granulometry analysis

J. Marcos, R. Nava, G. Cristobal, A. Munoz-Barrutia, B. Escalante-Ramírez, C. Ortiz-de-Solórzano

Proceedings Volume 8922, 892211 (2013) https://doi.org/10.1117/12.2035478

KEYWORDS: Emphysema, Lung, Tissues, Image segmentation, Statistical analysis, Lawrencium, Feature extraction, Analytical research, Binary data, Selenium

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Proceedings Article | 26 September 2013 Paper

Midbrain volume segmentation using active shape models and LBPs

Jimena Olveres, Rodrigo Nava, Boris Escalante-Ramírez, Gabriel Cristóbal, Carla María García-Moreno

Proceedings Volume 8856, 88561F (2013) https://doi.org/10.1117/12.2024396

KEYWORDS: Image segmentation, Binary data, Magnetic resonance imaging, Statistical modeling, Statistical analysis, Visualization, Image processing algorithms and systems, Volume rendering, Distance measurement, Brain

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Proceedings Article | 1 May 2012 Paper

A comprehensive study of texture analysis based on local binary patterns

Rodrigo Nava, Gabriel Cristóbal, Boris Escalante-Ramírez

Proceedings Volume 8436, 84360E (2012) https://doi.org/10.1117/12.923558

KEYWORDS: Binary data, Databases, Analytical research, Computer programming, Distance measurement, Statistical analysis, Light sources and illumination, Visualization, Feature extraction, Facial recognition systems

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One of the main goals of texture analysis is to provide a robust mathematical description of the spatial behavior of intensity values in any given neighborhood. These local distributions {called textures{ characterize object surfaces and are used for pattern identication and recognition of images. However, some spatial patterns may vary from quite simple stripes to randomness, where textures look like unstructured noise. Since textures can exhibit a large number of properties such as surface materials and geometry of the lighting sources, many dierent approaches have been proposed. A featured method is the modication of Wang's algorithm made by Ojala et al, the so-called local binary patterns (LBP). The LBP algorithm uses a 3×3 square mask named "texture spectrum" which represents a neighborhood around a central pixel. The values in the square mask are compared with the central pixel and then multiplied by a weighting function according with their positions. This technique has become popular due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. Specially, it has gained increased importance in image classication, where the success not only depends on a robust classier but also relies in a good selection of the feature descriptors. However, Ojala's algorithm presents some limitations such as noise sensitivity and lack of invariance to rotational changes. This fact has fostered many extensions of the original LBP approach that in many cases are based on minor changes in order to attain e.g. illumination and rotational invariance or improving the robustness to noise. In this paper we present a detailed overview of the LBP algorithm and other recently modications. In addition, we perform a texture classication study with seven algorithms in presence of rotational changes, noise degradation, contrast information, and dierent sizes of LBP masks using the USC-SIPI database. The LBP histograms have been evaluated using the Kullback-Leibler distance. This study will be a valuable insight for establishing a robust and ecient texture descriptor to solve real world problems.

SPIE Journal Paper | 26 March 2012 Open Access

Autofocus evaluation for brightfield microscopy pathology

Rafael Redondo, Gabriel Cristóbal, Gloria Bueno Garcia, Oscar Deniz, Jesus Salido, Maria del Milagro Fernandez, Juan Vidal, Juan Carlos Valdiviezo, Rodrigo Nava, Boris Escalante-Ramírez, Marcial Garcia-Rojo

JBO, Vol. 17, Issue 03, 036008, (March 2012) https://doi.org/10.1117/12.10.1117/1.JBO.17.3.036008

KEYWORDS: Microscopy, Pathology, Tissues, Cell biology, Image analysis, Breast, Biopsy, Lung, Computing systems, Image processing

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Proceedings Article | 3 November 2011 Paper

Evaluation of autofocus measures for microscopy images of biopsy and cytology

R. Redondo, M. G. Bueno, J. C. Valdiviezo, R. Nava, G. Cristóbal, M. García, O. Déniz, B. Escalante-Ramírez

Proceedings Volume 8011, 801194 (2011) https://doi.org/10.1117/12.903314

KEYWORDS: Microscopy, Biopsy, Cell biology, Computing systems, Picosecond phenomena, Biological research, Image analysis, Tissues, Image acquisition, Breast

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Proceedings Article | 5 May 2010 Paper

Blind quality assessment of multi-focus image fusion algorithms

Rodrigo Nava, Boris Escalante-Ramírez, Gabriel Cristóbal

Proceedings Volume 7723, 77230F (2010) https://doi.org/10.1117/12.853899

KEYWORDS: Image fusion, Image quality, Image processing, Wavelets, Visualization, Image information entropy, Data processing, Signal to noise ratio, Quality measurement, Image registration

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Proceedings Article | 19 May 2008 Paper

A novel multi-focus image fusion algorithm based on feature extraction and wavelets

Rodrigo Nava, Boris Escalante-Ramírez, Gabriel Cristóbal

Proceedings Volume 7000, 700028 (2008) https://doi.org/10.1117/12.781403

KEYWORDS: Image fusion, Wavelets, Feature extraction, Image filtering, Image processing, Image quality, Sensors, Image sensors, Image segmentation, Composites

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Proceedings Article | 22 May 2007 Paper

Nonreference image fusion evaluation procedure based on mutual information and a generalized entropy measure

Rodrigo Nava, Gabriel Cristóbal, Boris Escalante-Ramírez

Proceedings Volume 6592, 659215 (2007) https://doi.org/10.1117/12.722844

KEYWORDS: Image fusion, Image quality, Image information entropy, Image processing, Data fusion, Discrete wavelet transforms, Signal to noise ratio, Wavelets, Image resolution, Visualization

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