Post on 09-Jul-2022
Procesamiento de Imágenes y Aprendizaje Automático Federico Lecumberry
Departamento de Procesamiento de Señales Instituto de Ingeniería Eléctrica
Departamento de Procesamiento de Señales
20+ años de experiencia en
proyectos interdisciplinarios de investigación
en fundamentos y aplicaciones.
19 docentes
8 full time (DT)
10 PhD • 6 MSc
Procesamiento de Señales audio • imágenes • video
Visión Artificial
Teoría de la Información
Reconocimiento de Patrones • Aprendizaje Automático •
Deep Learning
Amplia red de colaboraciones
interdisciplinarias nacionales e
internacionales.
Enseñanza de grado y posgrado.
Agro • Biomedicina • Biometría • Detección de anomalías • Energía •
Music Retrieval • Multimedia • Remote Sensing • Robótica • Microscopía •
Restauración Digital
https://iie.fing.edu.uy/dps/ @IIE_FIng_UdelaR
Procesamiento de Imágenes + Aprendizaje Automático
Tailanián, M, et al. "Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques." 2014
�1�2�3
Detección de patologías sub-clínicas
Detección de pólipos en colonoscopía virtual
15/ 38
Feature extraction
Chosen features are based on clinical methods of diagnosis:
I 7 shape features such as symmetry about both axes.I 2 border features measuring lesion border hardness.I 36 color features - µ&� for RGB & HSV for each 3 regions.I 12 texture features - 4 statistics of GLCM1 for each 3 regions.
1Gray-Level Co-occurrence Matrix
Detección de melanomas en imágenes de dermatoscopía
Detección de floraciones algales en imágenes satelitales Estimación de parámetros carniceros
CAPITULO 4. OBTENCION DE MEDIDAS
Figura 4.4: Discriminacion entre lo que es grasa intramuscular y lo que no
Mp,q =NX
x=1
MX
y=1
xpyqI(x, y) (4.2)
Por ejemplo, el momento de orden 0 es
M0,0 =NX
x=1
MX
y=1
I(x, y) = A (4.3)
que corresponde al area del objeto.
Centro de Masa
La definicion del centro de masa, desde el punto de vista de la fısica es el puntode un objeto que tiene la misma cantidad de objeto en cualquier direccion. En el casodel tratamiento de imagenes se utiliza como punto de referencia del objeto y vienedado por
x =
PNx=1
PMy=1 xI(x, y)
A(4.4)
y =
PNx=1
PMy=1 yI(x, y)
A(4.5)
40
Procesamiento de Audio + Aprendizaje Automático
Análisis de estilos de ejecución del tambor
piano.
Pérdidas No-Técnicas (NTL) en consumo de energía eléctrica
Detección de Anomalías
Análisis de anomalías en redes celulares (QoS, fraud, links down, …)
Análisis de patrones rítmicos en el tambor
piano.
Original Reconstrucción Nariz Género
"Chivita"CejasLentes
Separación de factores de variación en la formación de imágenes
Deep Learning / Reinforcement Learning
Lezama, J. Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision. International Conference on Learning Representations, 2019.
Análisis y clasificación de entornos sonoros urbanos en tiempo real
Deep Metric Learning
Deep Video Deblurring
2017 Dublin City Noise Maps
Restauración de documentos
Texto
Video
Audio y música
+Biomedicina
Measuring the Length of Primary Cilia: anImageJ Plugin
Abstract We are developing a method for measuring the length of primary cilia in the 3D reconstructed volume. A stack
of images is acquired in a confocal microscope where several cilia are present. Taking this stack as input, the developed
algorithm identi�es each cilium and computes its length. The procedures includes the PSF deconvolution, Gaussian
convolution, skeleton computation and 3D curve �tting for extrapolation. Evaluation on synthetic data is presented. The
performance on real acquired images is currently under evaluation. The algorithm is implemented in Python and Java and it is
available as a Fiji/ImageJ plugin.
Institut Pasteurde Montevideo
1 Signal Processing Laboratory, Institut Pasteur de Montevideo2 Instituto de Ingeniería Eléctrica, Universidad de la República
Mauricio Ramos1, Martín Etchart1, Federico Lecumberry1,2
Block Diagram of Proposed Method
Stack ofImages
CiliaIdenti�cation
Re-slice and Gauss
Blurring
PSFDeconvolution
SkeletonComputation
3D CurveFitting and
Extrapolation Statisticsof Lengths
Conclusions and Future Work
References
Evaluation on Synthetic DataA synthetic dataset of 1200 cilia
was generated simulating the
acquisition procedure and were
processed with the proposed
method. The computed length
are compared with the real ones
with and without the curve
extrapolation.
1. S. Bolte & F. P. Cordelières. A guided tour into subcellular colocalization analysis in light microscopy. Journal of Microscopy, Volume 224, Issue 3: 213-232.
2. Lee et al. Building skeleton models via 3-D medial surface/axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462–478, 1994.
3. Saggese et al.: Development of a method for the measurement of primary cilia length in 3D. Cilia 2012 1:11. doi:10.1186/2046-2530-1-11
An algorithm for measuring the length of primary cilia was
developed and implemented as a plugin for ImageJ/Fiji. A
qualitative evaluation by an expert shows that the adjustment of
the paths is as expected. A quantitative evaluation using
simulated data presents a mean relative error lower than 3%
(approximately 200 nanometers). The processing time is a few
seconds per cilium, accelerating and systematizing its length
measurement. In future releases the results will be presented to
the user who may accept, discard or manually correct the
adjustment and measurement.
Histogram of errors in micrometers
Error in micrometers
Without curve extrapolationWith curve extrapolationGaussian �tting
PSF Deconvolution is performed using the Huygens
Essential deconvolution software (Scienti�c Volume
Imaging, Hilversum, The Netherlands).
Cilia Identi�cation is performed using the 3D
Objects Counter [1] ImageJ's plugin, which counts
the number of 3D objects in a stack satisfying some
constraints in volume and gray values.
Reslice and Gaussian Blurring is performed in order to
obtain an isotropic distribution of signal in the
reconstructed volume given by the image acquisition
process.
Skeleton Computation is performed on
an automatically thresholded cilium.
The initial skeleton computed [2]
generally presents several branches
which are pruned keeping the largest
shortest path.
3D Curve Fitting and Extrapolation. By de�nition a skeleton does not
reach the basal body and the furthest end. Therefore, a low order
polynomial curve is �tted to the 3D skeleton and its extrapolation allows
to measure the full length of the cilium.
1
23
4
5
56
7
8
9
10
001 frame 045
Automatic Cilia Identi�cation
xzy
Initial skeleton with several branches (thresholded cilium not shown).
Fitting and extrapolation
Before
x
z
After
x
z
Lengths are computed along the path
de�ned by the skeleton and its
extrapolation from both furthest points in
the thresholded cilium. Statistics can be
computed and automatically analyzed from
the processed stacks.
zx
y
x
yxz
y
Paths for length measurement, skeleton (in gray) and its extrapolation (in orange).
xz
y
x
y
Position X
Index
Skeleton pointCurve Fitting to skeletonCurve Fitting for extrapolation
Position Y
Index
Skeleton pointCurve Fitting to skeletonCurve Fitting for extrapolation
Position Z
Index
Skeleton pointCurve Fitting to skeletonCurve Fitting for extrapolation
median = -1.79standard deviation = 0.52
median = -0.15standard deviation = 0.40
Measuring the Length of Primary Cilia: anImageJ Plugin
Abstract We are developing a method for measuring the length of primary cilia in the 3D reconstructed volume. A stack
of images is acquired in a confocal microscope where several cilia are present. Taking this stack as input, the developed
algorithm identi�es each cilium and computes its length. The procedures includes the PSF deconvolution, Gaussian
convolution, skeleton computation and 3D curve �tting for extrapolation. Evaluation on synthetic data is presented. The
performance on real acquired images is currently under evaluation. The algorithm is implemented in Python and Java and it is
available as a Fiji/ImageJ plugin.
Institut Pasteurde Montevideo
1 Signal Processing Laboratory, Institut Pasteur de Montevideo2 Instituto de Ingeniería Eléctrica, Universidad de la República
Mauricio Ramos1, Martín Etchart1, Federico Lecumberry1,2
Block Diagram of Proposed Method
Stack ofImages
CiliaIdenti�cation
Re-slice and Gauss
Blurring
PSFDeconvolution
SkeletonComputation
3D CurveFitting and
Extrapolation Statisticsof Lengths
Conclusions and Future Work
References
Evaluation on Synthetic DataA synthetic dataset of 1200 cilia
was generated simulating the
acquisition procedure and were
processed with the proposed
method. The computed length
are compared with the real ones
with and without the curve
extrapolation.
1. S. Bolte & F. P. Cordelières. A guided tour into subcellular colocalization analysis in light microscopy. Journal of Microscopy, Volume 224, Issue 3: 213-232.
2. Lee et al. Building skeleton models via 3-D medial surface/axis thinning algorithms. Computer Vision, Graphics, and Image Processing, 56(6):462–478, 1994.
3. Saggese et al.: Development of a method for the measurement of primary cilia length in 3D. Cilia 2012 1:11. doi:10.1186/2046-2530-1-11
An algorithm for measuring the length of primary cilia was
developed and implemented as a plugin for ImageJ/Fiji. A
qualitative evaluation by an expert shows that the adjustment of
the paths is as expected. A quantitative evaluation using
simulated data presents a mean relative error lower than 3%
(approximately 200 nanometers). The processing time is a few
seconds per cilium, accelerating and systematizing its length
measurement. In future releases the results will be presented to
the user who may accept, discard or manually correct the
adjustment and measurement.
Histogram of errors in micrometers
Error in micrometers
Without curve extrapolationWith curve extrapolationGaussian �tting
PSF Deconvolution is performed using the Huygens
Essential deconvolution software (Scienti�c Volume
Imaging, Hilversum, The Netherlands).
Cilia Identi�cation is performed using the 3D
Objects Counter [1] ImageJ's plugin, which counts
the number of 3D objects in a stack satisfying some
constraints in volume and gray values.
Reslice and Gaussian Blurring is performed in order to
obtain an isotropic distribution of signal in the
reconstructed volume given by the image acquisition
process.
Skeleton Computation is performed on
an automatically thresholded cilium.
The initial skeleton computed [2]
generally presents several branches
which are pruned keeping the largest
shortest path.
3D Curve Fitting and Extrapolation. By de�nition a skeleton does not
reach the basal body and the furthest end. Therefore, a low order
polynomial curve is �tted to the 3D skeleton and its extrapolation allows
to measure the full length of the cilium.
1
23
4
5
56
7
8
9
10
001 frame 045
Automatic Cilia Identi�cation
xzy
Initial skeleton with several branches (thresholded cilium not shown).
Fitting and extrapolation
Before
x
z
After
x
z
Lengths are computed along the path
de�ned by the skeleton and its
extrapolation from both furthest points in
the thresholded cilium. Statistics can be
computed and automatically analyzed from
the processed stacks.
zx
y
x
yxz
y
Paths for length measurement, skeleton (in gray) and its extrapolation (in orange).
xz
y
x
y
Position X
Index
Skeleton pointCurve Fitting to skeletonCurve Fitting for extrapolation
Position Y
Index
Skeleton pointCurve Fitting to skeletonCurve Fitting for extrapolation
Position Z
Index
Skeleton pointCurve Fitting to skeletonCurve Fitting for extrapolation
median = -1.79standard deviation = 0.52
median = -0.15standard deviation = 0.40
Microscopía / Bioimaging
Viral evolution analisysFusion protein identification
Fluorescence Microscopy
Optical Microscopy Cryo-Electron Microscopy
Genetic sequence analysis
Centro Nacional de Documentación Musical Lauro Ayestarán
Carreras de grado
• Ingeniería Eléctrica (perfil Procesamiento de Señales y Aprendizaje Automático)
• Ingeniería en Sistemas de Comunicación (perfil Procesamiento de Señales)
• Ingeniería y Licenciaturas en Computación (Perfiles IA y Ciencia de Datos)
Actualización profesional
• Cursos de actualización en el área (IIE, IMERL, INCO)
• Especialización en Ciencia de Datos (CPAP-INCO)
Carreras y programas de formación con AA en FIng
Posgrados
• Maestría en Ingeniería Matemática
• Maestría en Ciencia de Datos Aplicada (CPAP-INCO)
• Maestría en Ciencia de Datos y Aprendizaje Automático
• Ingeniería Eléctrica (Maestría y Doctorado)
• Informática (Maestría y Doctorado, PEDECIBA)
Referencias
Bartesaghi, A., Lecumberry, F., Sapiro, G., & Subramaniam, S. (2012). Protein secondary structure determination by constrained single-particle cryo-electron tomography. Structure, 20(12), 2003-2013.
Capdehourat, G., Corez, A., Bazzano, A., Alonso, R., & Musé, P. (2011). Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern Recognition Letters, 32(16), 2187-2196.
Fiori, M., Musé, P., & Sapiro, G. (2014). A complete system for candidate polyps detection in virtual colonoscopy. International Journal of Pattern Recognition and Artificial Intelligence, 28(07), 1460014.
García González, G., Casas, P., Fernández, A., & Gomez, G. (2020). Net-GAN: Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. In TMA Conference 2020, Network Traffic Measurement and Analysis Conference, Berlin, Germany, 8-12 jun.. IEEE/IFIP.
Lezama, J. (2019). Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision. In ICLR (Poster).
Lezama, J., Qiu, Q., Musé, P., & Sapiro, G. (2018). Ole: Orthogonal low-rank embedding-a plug and play geometric loss for deep learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8109-8118).
LUISA, Leyendo Unidos para Interpretar loS Archivos. https://mh.udelar.edu.uy/luisa
Massaferro Saquieres, P., Di Martino, M., & Fernández, A. (2020). Fraud detection in electric power distribution: an approach that maximizes the economic return. IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 703-710, Jan. 2020.
Megrian, D., Aguilar, P. S., & Lecumberry, F. (2016, November). Similarity measure for cell membrane fusion proteins identification. In Iberoamerican Congress on Pattern Recognition (pp. 257-265). Springer, Cham.
Nunes, J. L., Piquerez, M., Pujadas, L., Armstrong, E., Fernández, A., & Lecumberry, F. (2015). Beef quality parameters estimation using ultrasound and color images. BMC bioinformatics, 16(S4), S6.
Rocamora, M. (2018). Computational methods for percussion music analysis: The Afro-Uruguayan Candombe drumming as a case study.
Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., & Wang, O. (2017). Deep video deblurring for hand-held cameras. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1279-1288).
Tailanián, M., Lecumberry, F., Fernández, A., Gnemmi, G., Meikle, A., Pereira, I., & Randall, G. (2014, November). Dairy cattle sub-clinical uterine disease diagnosis using pattern recognition and image processing techniques. In Iberoamerican Congress on Pattern Recognition (pp. 690-697). Springer, Cham.
Tambasco, F., Martínez, G., Simón, D., Moratorio, G., Fariello, M.I., Vignuzzi, M., & Lecumberry, F. (2018, November) Viral evolution analysis and visualization based on a Shannons Entropy approach. 2018 Institut Pasteur International Network Symposium: Combating Resistance : Microbes and Vectors, Paris, France, 15-16 nov, - 2018
Zinemanas, P., Cancela, P., & Rocamora, M. (2019, April). End-to-end convolutional neural networks for sound event detection in urban environments. In 2019 24th Conference of Open Innovations Association (FRUCT) (pp. 533-539). IEEE.
Muchas gracias lecumberry@fing.edu.uy