Towards Automated Bioimage Analysis: Algorithms & Applications - - PowerPoint PPT Presentation

João Manuel R. S. Tavares tavares@fe.up.pt www.fe.up.pt/~tavares

March 2, 2016

Towards Automated Bioimage Analysis: Algorithms & Applications

Outline

• Introduction
• Segmentation
• Analysis of Objects

– Tracking – Matching – Registration

• 3D Reconstruction
• Conclusions
• Research Team
• Publications & Events

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 2

Introduction

Presentation

• Associate Professor at FEUP (DEMec)
• Senior Research and Projects Coordinator of the

Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI)

• Habilitation in Mechanical Engineering from UP
• PhD and MSc degrees in Electrical and Computer

Engineering from FEUP

• BSc degree in Mechanical Engineering from FEUP
• Research Areas: Image Processing and Analysis,

Biomedical Imaging, Biomechanics, Human Posture and Control, Product Development

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 4

Introduction

• The researchers of Image Processing and

Analysis aim the development of algorithms to perform fully or semi-automatically tasks performed by the (quite complex) human vision system

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 5

Original images Computational 3D voxelized and poligonized models built

Azevedo et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(3):359-369

Introduction

• Image processing and analysis are topics of the

most importance for our Society

• Algorithms of image processing and analysis are

frequently used, for example, in:

– Natural Sciences – Sports – Biology – Industry – Engineering – Medicine

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 6

Introduction

• Examples of common tasks involving algorithms
• f image processing and analysis:

– Noise removal – Geometric correction – Segmentation, recognition (2D-4D) – Motion and/or deformation tracking and analysis, including matching and registration – 3D reconstruction – Assisted medical diagnosis and intervention

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 7

• Image: a matrix with n rows and m columns (and l

in 3D), being each basic element known as pixel (or voxel in 3D)

Introduction

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 8

• Image Processing: by applying mathematical
• perations/rules using the values of the image

pixels (or voxels in 3D) in the Cartesian or in another domain

Introduction

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 9

∗ −1 −2 −1 +1 +2 +1 ⎡ ⎣ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ = Gy ∗ −1 +1 −2 2 −1 +1 ⎡ ⎣ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ = Gx Gx

Sobel operator

∗

( denotes convolution)

• Image Acquisition: a sensor captures the

energy reflected or emitted by the imaged object

Introduction

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 10 http://what-when-how.com/introduction-to-video-and-image-processing/image-acquisition-introduction-to- video-and-image-processing-part-1

• Difficulties: noise, artifacts, occlusion, poor

illumination, reflections, complex objects and backgrounds

Introduction

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 11

https://rahaddadi.files.wordpress.com/2011/05/face_black_and_white_optical_illusion_cool-s453x562-92306-5803.jpg http://s1.cdn.autoevolution.com/images/news/the-longest-traffic-jam-in-history-12-days-62-mile-long-47237_1.jpg

Introduction

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 12

Image(s) enhancement / correction Image(s) segmentation / features extraction tracking matching Image(s) motion analysis registration image (pre)processing image analysis / computational vision 3D vision computer vision

Usual Pipeline

Segmentation

Segmentation

• It is intended to identify in a fully or semi-

automatically manner objects (2D/3D) presented in static images or in image sequences

• The most usual methodologies are based on

thresholding, region growing, template matching, statistical, geometric or physical modeling, or artificial classifiers

• It is one of the most usual operations involved in

the computational analysis of objects in images

• Frequent problems: noise, artifacts, low resolution,

reduced contrast, shapes not previously known,

• cclusion, multiple objects, etc.

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 14

Segmentation

• Image segmentation by threshold (binarization)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 15 Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

Segmentation

• Example: segmentation of contours in dynamic

pedobarography (Otsu’s method, morphologic dilation,

xor operation)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 16

Original images Segmented images

Bastos & Tavares (2004) Lecture Notes in Computer Science 3179:39-50

reflected light glass pressure

• paque layer

lamp lamp transparent layer

Segmentation

• Image segmentation by region growing

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 17 Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

Segmentation

• Example: segmentation of ear structures (region

growing)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 18

Segmentation obtained (bony labyrinth)

Barroso et al. (2011) CNME 2011 Ferreira et al. (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(8):888-904

Original Image

Segmentation

• Segmentation of objects based on deformable

templates

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 19 Carvalho & Tavares (2006) CompIMAGE 2006, 129-134 Carvalho & Tavares (2007) VipIMAGE 2007, 209-215

Example of a deformable template (for the eye)

Segmentation

• Example: segmentation of eye

features (deformable geometric template)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 20

Original image and associated force (or energy) fields Segmentation of the iris using a deformable template (a circle) Segmentation of an eye using an deformable template

Carvalho & Tavares (2006) CompIMAGE 2006, 129-134 Carvalho & Tavares (2007) VipIMAGE 2007, 209-215

Segmentation

• Segmentation of based on active shape models

(point distribution models, optimization)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 21 Vasconcelos & Tavares (2008) Computer Modeling in Engineering & Sciences 36(3):213-241

Segmentation

• Example: analysis of the vocal tract during

speech production from MR images (active shape model)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 22

Original image Final segmentation

Vasconcelos et al. (2011) Journal of Voice 25(6):732-742

• Segmentation of objects based on active

contours (i.e. snakes – parametric models)

Segmentation

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 23 Tavares et al. (2009) International Journal for Computational Vision and Biomechanics 2(2):209-220

Esnake = Eint

s=0 1

∫

(v(s))+ Eext(v(s))ds Eint = α(s) dv(s) ds

2

+ β(s) d 2v(s) ds2

2

Segmentation

• Example: segmentation of medical images

(active contours - snakes)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 24

Initial contour Final contour

Tavares et al. (2009) International Journal for Computational Vision and Biomechanics 2(2):209-220 Gonçalves et al. (2008) Computer Modeling in Engineering & Sciences 32(1):45-55

Segmentation

• Segmentation of objects based on the level-set

method (geometrical models)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 25 Ma et al. (2010) Medical Engineering & Physics 32(7):766-774 Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

∂φ ∂t + F Δφ = 0

Typical form of the motion equation:

• Example: segmentation of skin pigmented

lesions in dermoscopic images (color spaces, level-

set model)

Segmentation

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 26

Segmentation examples under different imaging conditions and different types of skin pigmented lesions Illustration of the segmentation process

Ma & Tavares (2015) IEEE J. of Biomedical and Health Informatics, DOI: 10.1109/JBHI.2015.2390032 (in press) Filho et al. (2015) Journal of Medical Systems 39(11):177

Segmentation

• Segmentation of objects based on the level set

method with a prior knowledge

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 27 Ma et al. (2010) Medical Engineering & Physics 32(7):766-774 Ma et al. (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(2):235-246

Segmentation

• Example: segmentation simultaneously of three
• rgans of the female pelvic cavity in MRI

images (level-set method, a prior knowledge)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 28 Ma et al. (2013) Computers in Biology and Medicine 43(4):248-258 Ma et al. (2012) The Int. Journal for Numerical Methods in Biomedical Engineering 28(6-7):714-726

Segmentation of the bladder, vagina and rectum in pelvic cavity images (3 examples)

Analysis of Objects: Tracking

Tracking

• It is intended to track the motion and/or the

deformation of objects in image sequences

• In this area, the methodologies based on optical

flow, block matching and stochastic methods are widespread

• Usually, it concerns the estimation of the

motion/deformation involved, the management

• f the features being tracked, the analysis of the

motion tracked and its quantification

• Usual problems: non-rigid motions, geometric

distortion, non-constant illumination, occlusion, noise, multiple objects, etc.

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 30

Tracking

• Computational framework

to track features in image sequences (Kalman Filter or

Unscented Kalman Filter,

• ptimization, Mahalanobis

distance, management model)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 31 Pinho et al. (2007) Int. Journal of Simulation Modelling 6(2):84-92 Pinho & Tavares (2009) VipIMAGE 2009, 299-304 Pinho & Tavares (2009) Computer Modeling in Engineering & Sciences 46(1):51-75

Tracking

• Example: tracking marks in gait analysis (Kalman

filter, Mahalanobis distance, optimization, management model)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 32

Prediction Uncertainty Area Measurement Correspondence Result (5 frames)

Pinho et al. (2005) ICCB 2005, 915-926 Pinho & Tavares (2009) Computer Modeling in Engineering & Sciences 46(1):51-75

Tracking

• Example: tracking mice in long image

sequences (Kalman filter, Mahalanobis distance,

• ptimization, management model)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 33

(547 frames)

Pinho et al. (2005) LSCCS, Vol. 4A:463-466 Pinho et al. (2007) International Journal of Simulation Modelling 6(2):84-92

Analysis of Objects: Matching

Matching

• The goal is to establish correspondence

between image features

• It is regularly used in the computational analysis of
• bjects in images, for example, to register (i.e.

align) objects, recognize objects, attain 3D information, analyze tracked motion, etc.

• Generally, it is achieved by considering

invariant characteristics, as curvature, or displacements in global/eigen spaces (like in modal space)

• Common problems: occlusion, non-rigid

deformations, high shape variations, etc.

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 35

Matching

• Based on physical or geometrical modeling and

modal matching

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 36

Modeling (physical or geometrical) Eigenvalues / eigenvectors computation Matching matrix assembly Contour 1 Contour 2 Matches achievement (optimization) Modeling (physical or geometrical) Eigenvalues / eigenvectors computation

Bastos & Tavares (2006) Inverse Problems in Science and Engineering 14(5):529-541 Tavares & Bastos (2010) Progress in Computer Vision and Image Analysis 339-368

Matching

• Example: matching contours and surfaces in

dynamic pedobarography (FEM modeling, modal

analysis, optimization)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 37

Image of dynamic pedobarography

Tavares & Bastos (2005) Electronic Letters on Computer Vision and Image Analysis 5(3):1-20

Matching found between two contours Matching found between two intensity (pressure) surfaces (2 views) Matching found between iso-contours (2 views)

Analysis of Objects: Registration

Registration

• It is commonly required in order to compare
• bjects acquired at different time instants or

according to distinct conditions

• It is essential, for example, in Medicine to follow

up the evaluation of diseases

• Usually, it is achieved by considering characteristic

features, as points of high curvature, and their matching, or by minimization of a similarity measure, followed by the estimation of the involved transformation

• Common problems: key and invariant features not

easily detected, occlusion, non-rigid deformations, severe shape variations, etc.

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 39

Registration

• Registration based on contours matching,
• ptimization and dynamic programming

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 40

The cost matrix is built based on geometric or physical principles The matching is found based on the minimization

• f the sum of the costs

associated to the possible correspondences To search for the best matching is used an

• ptimization assignment

algorithm

Bastos & Tavares (2006) Inverse Problems in Science and Engineering 14(5):529-541 Oliveira & Tavares (2009) Computer Modeling in Engineering & Sciences 43(1):91-110 Oliveira, Tavares, Pataky (2009) Journal of Biomechanics 42(15):2620-2623

Registration: 2D, monomodal, intrasubject Processing time: 0.5 s (AMD Turion64, 2.0 GHz, 1.0 GB of RAM) Images dimension: 217x140 pixels

Fixed image and contour (MRI) Moving image and contour (MRI) Overlapped images before the registration Overlapped images after the registration Difference between the images after the registration Correspondences found between the Corpus Callosum contours Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93

Registration

• Example: registration of brain MR images

(geometrical modeling, matching, optimization, dynamic programming)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 41

Registration

• Registration of images based on Fourier transf.

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 42

Original images Images before and after the registration

Oliveira, Pataky, Tavares (2010) Computer Methods in Biomechanics and Biomedical Engineering 13(6):731-740

Registration

• Registration based on the iterative search for the

parameters of the transformation that optimizes a similarity measure between the input images

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 43 Moving image Fixed image Pre-registration transformation (optional) Interpolator Similarity measure Optimizer Geometric transformation

The optimization algorithm stops when a similarity criterion is achieved

Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93

Registration: 2D, multimodal, intrasubject (without pre-registration) Similarity measure: MI Processing time: 4.6 s (AMD Turion64, 2.0 GHz, 1.0 GB of RAM) Images dimension: 246x234 pixels

Oliveira & Tavares (2014) Computer Methods in Biomechanics and Biomedical Engineering 17(2):73-93

Registration

• Example: registration/fusion of head CT/MR

images (optimization of a similarity measure)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 44

“Checkerboard” of the slices before the registration (CT/MRI-PD, brain)

F F F F F F F F M M M M M M M M

(The “checkerboard” slice is built by interchanging square patches of both slices and preserving their original spatial position in the fixed (F) and moving (M) slices)

Registration

• Example: registration/fusion using iterative optimization

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 45

Registration: 3D, multimodal, intrasubject; Similarity measure: MI Checkerboard of the slices after the registration (CT/MRI-PD, brain)

Registration

• Example: registration/fusion using iterative opt. (cont.)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 46

Normal Alzheimer Idiopathic Parkinsonism Essential tremor

Registration

• Example: application on brain DaTSCAN SPECT

images

Brain DaTSCAN SPECT images are used to assist the diagnosis of the Parkinson’s disease and to distinguish it from other degenerative

• diseases. The solution developed is able to:

– Segment the relevant areas and perform dimensional analysis – Quantify the binding potential of the basal ganglia – Computation of statistical data relatively to a reference population – Image classification for diagnosis purposes

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 47

Mean slice from the population used as reference Corresponding slice of a patient Difference of intensities Z-scores mapping over the slice (red – high Z-

scores) (The blue rectangles represent the 3D ROIs used to compute the binding potentials) Oliveira et al. (2014) The Quarterly Journal of Nuclear Medicine and Molecular Imaging 58(1):74-84

Registration

• Example: application on brain DaTSCAN SPECT

images

3D volume images are automatically registered and statistical analysis relatively to a reference population can be attained

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 48

Registration

• Registration of image sequences: spatial &

temporal registration

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 49 Moving sequence Fixed sequence Apply the spatio & temporal transformation Compute the similarity measure Optimizer Build the spatio & temporal transformation Oliveira et al. (2011) Medical & Biological Engineering & Computing 49(7):843-850 Oliveira & Tavares (2013) Medical & Biological Engineering & Computing 51(3):267-276 Build the temporal representative images Search for the transformation that register the temporal representative images Estimate the linear temporal registration Pre-registration Registration optimization

Registration

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 50

• Example: registration of image sequences in

dynamic pedobarography (spatial & temporal

registration)

Device: Light reflection (25 fps, resolution 30 pixels/cm2) Image similarity measure: MSD Sequences dimension: 160x288x22, 160x288x25 Processing time: 1 min (using an AMD Turion64, 2.0 GHz, 1.0 GB of RAM) Template sequence Source sequence Overlapped sequences Before the registration After the registration

3D Reconstruction

3D Reconstruction

• It is intended to accomplish the 3D reconstruction of
• bjects or scenes from images
• In this area, the following methodologies are common:

inner shapes – 2D segmentation (contours, for example) and data interpolation, or 3D segmentation; external shapes – active techniques (with energy projection or relative motion), passive techniques (without energy projection nor relative motion) and of space carving; deformation of 3D deformable models prebuilt

• Usually, it involves tasks of camera calibration, data

segmentation, matching, triangulation, registration and fusion

• Common problems: geometric distortion, insufficient or

unstable illumination, occlusion, noise, multiple objects, complex shapes and topologies, etc.

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 52

3D Reconstruction

• 3D reconstruction of organs from medical images

based on 2D segmentation, loft (marching cubes) and smooth

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 53

Reconstructed organs from pelvic cavity

Pimenta et al. (2006) CompIMAGE 2006, 343-348 Alexandre et al. (2007) VipIMAGE 2007, 359-362

3D Reconstruction

• Example: 3D reconstruction from multiple

views (registration/fusion)

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 54

Axial and sagittal T2-weighted MR images 3D Reconstruction of the bladder by fusion data from the axial and sagittal images (2 views)

Ma et al. (2013) Medical Engineering & Physics 35(12):1819-1824

Conclusions

Conclusions

• The area of image processing and analysis is very

complex and demand, but of raised importance in many domains

• Numerous hard challenges exist, as for example, adverse

conditions in the image acquisition process, occlusion,

• bjects with complicate shapes, with topological variations
• r undergoing complex motion or deformation
• Considerable work has already been developed, but

important and complex goals still to be reached

• Methods and methodologies of other research areas,

as of Mathematics, Computational Mechanics, Medicine and Biology, can contribute significantly for their reaching

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 56

The work presented has been done with the support of Fundação Gulbenkian, CNPq, CAPES, and FCT, mainly trough the funding of the research projects:

– PTDC/BBB-BMD/3088/2012 – PTDC/SAU-BEB/102547/2008 – PTDC/SAU-BEB/104992/2008 – PTDC/EEA-CRO/103320/2008 – UTAustin/CA/0047/2008 – UTAustin/MAT/0009/2008 – PDTC/EME-PME/81229/2006 – PDTC/SAU-BEB/71459/2006 – POSC/EEA-SRI/55386/2004

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 57

Acknowledgments

Research Team

(Image Processing and Analysis)

Research Team (Image Processing and Analysis)

• Post-Doc students (5):

– Finished: Alexandre Carvalho, Simone Prado, Mercedes Filho – In course: Zhen Ma; Pedro Predosa

• PhD students (15):

– Finished: Zhen Ma, Francisco Oliveira, Teresa Azevedo, Daniel Moura, Sandra Rua, Maria Vasconcelos – In course: João Nunes, Alex Araujo, Carlos Gulo, Roberta Oliveira, Danilo Jodas, Pedro Morais, Andre Pilastri, Nuno Sousa, Domingos Vieira

• MSc students (30):

– Finished: Raquel Alves, Carolina Tabuas, Jorge Pereira, Luis Ribeiro, Luis Ferro, Rita Teixeira, Liliana Azevedo, Diana Cidre, Célia Cruz, Priscila Alves, Pedro Gomes, Nuno Sousa, Diogo Faria, Elisa Barroso, Ana Jesus, Frederico Jacobs, Gabriela Queirós, Daniela Sousa, Francisco Oliveira, Teresa Azevedo, Maria Vasconcelos, Raquel Pinho, Luísa Bastos, Cândida Coelho, Jorge Gonçalves, Frederico Junqueira, Jessica Delmoral, Ricardo Le – In course:

• BSc students (2)

– Finished: Ricardo Ferreira, Soraia Pimenta

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 59

Publications & Events

Taylor & Francis journal “Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization”

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 61

www.tandfonline.com/tciv

Springer book series “Lecture Notes in Computational Vision and Biomechanics (LNCV&B)” Editors: João Manuel R. S. Tavares, Renato Natal Jorge

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 62

www.springer.com/series/8910

Indexed in

Events

@2016 João Manuel R.S. Tavares Towards Automated Bioimage Analysis: Algorithms & Applications 63

Towards Automated Bioimage Analysis: Algorithms & Applications

João Manuel R. S. Tavares tavares@fe.up.pt www.fe.up.pt/~tavares

March 2, 2016

Thank You!