Lecture

Digital Images and Visual Perception

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In course

BIO-695: Image Processing for Life Science

Registration details will be announced via email. It takes place yearly from Sept./October to December & intends to teach image processing with a strong emphasis of applications in life sciences. The idea is to enable the participants to solve image-proces ...

Description

This lecture series covers the basics of digital images, focusing on image processing and analysis. It explores the characteristics of digital images, how they are formed and displayed, and the impact on visual perception. The instructor introduces Fiji/ImageJ software for practical examples and exercises, emphasizing its accessibility and cost-free nature.

In MOOC

Image Processing and Analysis for Life Scientists

Nowadays, image-based methods are indispensable for life scientists. Light microscopy especially, has evolved from sketched out observations by eye, to high throughput multi-plane, multi-channel, multi-position and multimode acquisitions that easily produce thousands of information-rich images that must be quantified somehow to answer biological questions. This course will teach you core concepts from image acquisition to image filtering and segmentation, to help you tackle simple image analysis workflows

Instructors (4)

Olivier Burri

Expertise Development of bioimage analysis workflows and algorithms to extract quantitative data from microscopy imagesTools & Software: ImageJ/Fiji, CellProfiler, ilastik, Imaris, Arivis, Matlab, RStudio, IntelliJ, Eclipse, Jupyter NotebooksProgramming languages:Java, Groovy, JavaScript, TypeScript, PHP, Python, R, C, C , Matlab, Bash Expertise Development of bioimage analysis workflows and algorithms to extract quantitative data from microscopy imagesTools & Software: ImageJ/Fiji, CellProfiler, ilastik, I

Arne Seitz

04/1996-06/2000 Scientific co-worker at Philipps-University of Marburg, Dep. of Physical Chemistry 07/2000-10/2002 Post Doc at Max-Planck Unit for Structural Molecular Biology in Hamburg 11/2002-11/2005 Post Doc at European Molecular Biolohy Laboratory (EMBL), Cell Biology and Cell Biophysics Programme 11/2005-03/2009 Staff Scientist at Advanced Light Microscopy Facility, EMBL 04/2009- Head of Bioimaging and Optics platforme (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL)

Romain Guiet

Expertise Development of BioImage Analysis workflows and algorithms to extract quantitative data from microscopy images.BioSample Preparation : in vitro cell, organoid, time-lapse, fixation, staining strategies, actin, mitochondria, fluorescent protein expressionBioImage Acquisition : Widefield, Confocal, Whole Slide Imaging, Super-resolution (SIM, STORM, STED), FRET, FRAP, FLIMBioImage Analysis: Colocalisation analysis, Deconvolution, Time-lapse , Whole Slide ImagingTools & Software: ImageJ/Fiji, QuPath, i

Nicolas René Chiaruttini

Official source

Related concepts (16)

Digital image processing

Digital image processing is the use of a digital computer to process s through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over . It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing. Since images are defined over two dimensions (perhaps more) digital image processing may be modeled in the form of multidimensional systems.

Image analysis

Image analysis or imagery analysis is the extraction of meaningful information from s; mainly from s by means of techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face. Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information.

Digital image

A digital image is an composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis, respectively. Depending on whether the is fixed, it may be of vector or raster type. Raster image Raster images have a finite set of digital values, called picture elements or pixels.

Digital imaging

Digital imaging or digital image acquisition is the creation of a digital representation of the visual characteristics of an object, such as a physical scene or the interior structure of an object. The term is often assumed to imply or include the , , , printing and display of such images. A key advantage of a , versus an analog image such as a film photograph, is the ability to digitally propagate copies of the original subject indefinitely without any loss of image quality.

Image segmentation

In and computer vision, image segmentation is the process of partitioning a into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

Related lectures (64)

Digital Images and Visual Perception MOOC: Image Processing and Analysis for Life Scientists

Covers the definition of digital images, image formation, classification by bit depth, and image analysis.

Digital Images and Visual Perception MOOC: Image Processing and Analysis for Life Scientists

Emphasizes the reliability of digital image analysis over human visual perception.

X-ray Tomography Practical Analysis

Covers the practical analysis of X-ray tomography images using Fiji software.

Notation and key concepts

Covers the notation and key concepts related to digital images and 2D signal representations.

Image Processing Fundamentals