Lecture

Greyscale, Color and Special Images

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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 covers the different types of images, including Greyscale, color, and special formats. It explains the bit depth of Greyscale images, the storage and display of colored images, and the significance of different bit depths. The instructor demonstrates the differences between 8-bit and 16-bit images, the use of binary images for object detection, and the importance of understanding image formats in scientific analysis. Additionally, the lecture explores the creation of RGB images, the use of look-up tables for color scales, and the challenges of handling multiple channels in image processing. It concludes with an overview of file formats and the OME plugin for reading various image formats.

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 (26)

Channel (digital image)

Color digital images are made of pixels, and pixels are made of combinations of primary colors represented by a series of code. A channel in this context is the grayscale image of the same size as a color image, made of just one of these primary colors. For instance, an image from a standard digital camera will have a red, green and blue channel. A grayscale image has just one channel. In geographic information systems, channels are often referred to as raster bands.

Object detection

Object detection is a computer technology related to computer vision and that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including and video surveillance. It is widely used in computer vision tasks such as , vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation.

Raw image format

A camera raw image file contains unprocessed or minimally processed data from the of either a digital camera, a motion picture film scanner, or other . Raw files are so named because they are not yet processed, and contain large amounts of potentially redundant data. Normally, the image is processed by a raw converter, in a wide-gamut internal color space where precise adjustments can be made before to a viewable file format such as JPEG or PNG for storage, printing, or further manipulation.

RGB color model

The RGB color model is an additive color model in which the red, green and blue primary colors of light are added together in various ways to reproduce a broad array of colors. The name of the model comes from the initials of the three additive primary colors, red, green, and blue. The main purpose of the RGB color model is for the sensing, representation, and display of images in electronic systems, such as televisions and computers, though it has also been used in conventional photography.

Color management

In digital imaging systems, color management (or colour management) is the controlled conversion between the color representations of various devices, such as s, digital cameras, monitors, TV screens, film printers, computer printers, offset presses, and corresponding media. The primary goal of color management is to obtain a good match across color devices; for example, the colors of one frame of a video should appear the same on a computer LCD monitor, on a plasma TV screen, and as a printed poster.

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