Detailed Program Image Processing Summer School 2010
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1 Detailed Program Image Processing Summer School 2010 Monday : Registration : Introduction to image processing (Peter Horvath) Basic definitions (digital image, bit depth, sampling, ) What is image processing? Histogram transformation Mathematical morphology Filtering, convolution, Fourier transform Segmentation Shape feature extraction Tracking Registration Deconvoluiton Screening : Point operations and histogram transformations (Kalman Palagyi) Image operations (point, local, global) Examples of point operations: arithmetic operations, image negatives, pseudo coloring, log transform, power-low transform, gamma correction, contrast stretching, thresholding, gray-level slicing Histogram (normalized histogram, accumulated histogram) Point operations driven by histograms: histogram stretching, histogram equalization, local histogram equalization : Filtering and Fourier transform (Kalman Palagyi) Noise Convolution Filtering/smoothing in image space: (weighted) averaging filter, medial filter, sharpening, erosion, dilation, opening, closing, morphological filtering Fourier transform Convolution theorem Filtering/smoothing in frequency domain: low-pass filters, high-pass filters, bandpass filters, bandreject filters, periodic noise reduction : MATLAB/ImageJ installation, usage/programming tutorial (MM1: Peter Horvath, Aaron Ponti; MI1: Kota Miura, Csaba Balazs)
2 MatLab: o Installation o Programming basics (data types, vectors, matrices) o Installation o ImageJ environment o Basic image processing (histograms) : MATLAB/ImageJ usage/programming tutorial (MM1: Peter Horvath, Aaron Ponti; MI1: Kota Miura, Csaba Balazs) MatLab programming: o Scripts o Functions o Point operations o Filtering o Frequency domain operations : MATLAB/ImageJ usage/programming tutorial (MM1: Peter Horvath, Aaron Ponti; MI1: Kota Miura, Csaba Balazs) MatLab: o Basic image processing operations (histogram, filtering) o Introduction to macro scripting Tuesday : Image segmentation (Aaron Ponti) Simple thresholding Histogram-based segmentation Clustering-based approaches Adaptive thresholding Edge detection Watershed segmentation Markov random fields Manual segmentation Region-growing Active contours : Feature extraction and shape representation (Kalman Palagyi) Contour-based shape representation: chain code, signatures Region-based shape features: features in regionprops (Matlab), skeletons, distance transform,
3 moments Gray-scale features: moments, co-occurrence matrix, Haralick texture features : Registration/Stitching (Olaf Ronneberger) Registration o Motivation for Registration o Classification of different registration algorithms o basic Similarity measures o Transformations, transformation parameters, parameter space o Fast registration algorithms Advanced Registration o Advanced similarity measures o Elastic registration Stitching : Registration and stitching (TM1: Olaf Ronneberger, Peter Horvath; TI1: Aaron Ponti, Mario Emmenlauer) Gray-level based registration Similarity measure function, transformation function, best neighbor optimizer Manual Landmark-Based registration : Image segmentation (TM2: Aaron Ponti, Alessandra Griffa; TI2: Mark Longair, Kota Miura) Manual and automated thresholding Edge detection using filters and the Canny method Watershed algorithm for object separation Active contours Trainable segmentation : Feature extraction (TM3: Peter Horvath, Alessandra Griffa; TI3: Mark Longair, Kota Miura) Binary feature extraction o Location, area, shape, o Shape moments Grayscale features o Entropy, mean, std intensity o Haralick features
4 Wednesday : Deconvolution (Alessandra Griffa) Introduction to deconvolution Image formation model (PSF, OTF) Simple deconvolution approaches (deblurring, inverse filtering and regularization) Deconvolution algorithms (iterative algorithms, blind deconvolution) PSF widening (how the PSF changes in function of the microscope settings; spherical aberration) PSF generation: theoretical vs experimental PSF Deconvolution pre-processing (background and bleaching correction) Images acquisition tips Deconvolution examples : Tracking (Ivo Sbalzarini) : Imaging for Modeling and Simulation in Cell Biology (Petros Koumoutsakos) importance of imaging in computational model validation importance of the development of computational models from images via reverse engineering processes simulations of diffusion-reaction processes reverse engineering of a tug of war model for intracellular virus transport : Deconvolution (WM1: Aaron Ponti, Serge Pelet; WI1: Alessandra Griffa, Pascal Lorentz) Importance of the PSF Inverse filtering Iterative algorithms (Richardson-Lucy) 3D deconvolution Huygens Remote Manager demonstration : Tracking (WM2: Serge Pelet, Aaron Ponti; WI2: Peter Horvath, Pascal Lorentz) Thursday (Company day) : Bitplane : Perkin Elmer : Coffee break
5 : Definiens : MathWorks : Visualization Sciences Group : Colocalization (Gabor Csucs) Colocalization and image aquisition Quantitative colocalization Pearson coefficient Manders coefficient The Costes approach Friday : CellProfiler (Peter Horvath) Motivation, environment Pipeline system Modules Case study : Screening (Thomas Walter) : Detecting phenotypes in live-cell imaging-based screening (Daniel Gerlich) Supervised machine learning of cellular morphologies Single-cell tracking and hidden Markov model of cellular dynamics Assays with multiple fluorophores to score timing phenotypes CellCognition open source software for live-imaging-based screening Application: RNAi screen for mitotic exit phosphatases ; : Advanced programming I. and II. (FM1: Aaron Ponti; FI1: Mark Longair) Visualization Creating graphical user interfaces Object-oriented programming Interfacing with Java Interfacing with C/C++
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