Procesamiento de Imágenes y Aprendizaje Automático

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

https://twitter.com/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


Position Z

Index


median = -1.79standard deviation = 0.52


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


Position Y

Index


Position Z

Index




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 [email protected]

Procesamiento de Imágenes y Aprendizaje Automático

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