link Источник: www.edx.org
list 7 последовательности
assignment Уровень : Средний
chat_bubble_outline Язык : английский
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Резюме

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 on your own. All examples use open source solutions, in order to allow you to be independent from commercial solutions. Emphasis is made on good practices and typical pitfalls in image analysis. At the end of this course, you will be able to adapt and reuse workflows to suit your specific needs and be equipped with the tools and knowledge to adapt and seek advice from the ever-growing image analyst community of which you will be a part now

The course is taught by senior image analysts with longtime work experience in a service-oriented core facility.

  • Recall digital image formation principles
  • Understand human perception and color
  • Distinguish between bit-depths
  • Use lookup tables
  • Perform mathematical operations on images
  • Apply filtering to digital images
  • Understand and use image segmentation techniques
  • Create regions of interest and extract results from segmented images
  • How to perform projections and reslicing on images for analysis
  • Applying color deconvolution to brightfield images
  • Understand the concepts of the ImageJ Macro language

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Программа

Week 1: Digital Images
Introduction to digital image formation and how optical systems go from objects to images.

Week 2: Colors
Review of human visual perception and the RGB color model. Introduction to the concepts of image bit-depth and lookup tables.

Week3: Operating on Images
Introduction to image scaling, interpolation, and mathematical operations of images, and why certain bit-depths are more suitable than others.

Week4: Filtering
Using image filtering to enhance or suppress features in an image for easing subsequent analysis. We cover linear, nonlinear and Fourier filtering with emphasis on examples.

Week 5: Image Segmentation
Introduction to image segmentation and overview of available methods (thresholding, clustering, machine learning) and morphological operations.

Week 6: Regions of Interest
Going from analyzed objects to regions of interest and results tables. Emphasis is made on how to best obtain unbiased measurements and produce a reusable image analysis workflows

Week 7: Colors, and dimensionality reduction
Introduction to color models, and color deconvolution. Overview of the concept of dimensionality reduction through image projections and reslicing and application to measuring moving objects.

Extra Week: ImageJ Macro Programming Prime
Presentation of basic programming principles applied to the ImageJ Macro Language. Crash course on variables arrays, loops, conditionals, available macro functions and writing custom functions.

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Пользователи

Arne Seitz
Dr. rer. nat.
École polytechnique fédérale de Lausanne

Romain Guiet
Dr.
École polytechnique fédérale de Lausanne

Olivier Burri
Mr
École polytechnique fédérale de Lausanne

Nicolas Chiaruttini
Dr.
Ecole Polytechnique Fédérale de Lausanne (EPFL)

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Разработчик

École Polytechnique Fédérale de Lausanne

L’École polytechnique fédérale de Lausanne (EPFL) est une institution universitaire de renommée internationale, spécialisée dans le domaine de la science et de la technologie, située à Lausanne, bien que sur le territoire communal d'Écublens, en Suisse et fondée en 1853, sous le nom d’École spéciale de Lausanne.

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Платформа

Edx

EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.

EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.

Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.

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