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Lecture

Detecting Objects: Independent Channels

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 explains the importance of having independent channels when detecting objects in images. Using the example of cell staining, the instructor demonstrates how relying on a single channel can lead to underestimating negative cells and overestimating positive cells, emphasizing the need for a more accurate approach.

In MOOC

Image Processing and Analysis for Life Scientists

Instructors (4)

Olivier Burri

Nicolas René Chiaruttini

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

Official source

Quiz

Related concepts (15)

Staining

Staining is a technique used to enhance contrast in samples, generally at the microscopic level. Stains and dyes are frequently used in histology (microscopic study of biological tissues), in cytology (microscopic study of cells), and in the medical fields of histopathology, hematology, and cytopathology that focus on the study and diagnoses of diseases at the microscopic level. Stains may be used to define biological tissues (highlighting, for example, muscle fibers or connective tissue), cell populations (classifying different blood cells), or organelles within individual cells.

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.

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.

Endospore staining

Endospore staining is a technique used in bacteriology to identify the presence of endospores in a bacterial sample. Within bacteria, endospores are protective structures used to survive extreme conditions, including high temperatures making them highly resistant to chemicals. Endospores contain little or no ATP which indicates how dormant they can be. Endospores contain a tough outer coating made up of keratin which protects them from nucleic DNA as well as other adaptations.

Papanicolaou stain

Papanicolaou stain (also Papanicolaou's stain and Pap stain) is a multichromatic (multicolored) cytological staining technique developed by George Papanicolaou in 1942. The Papanicolaou stain is one of the most widely used stains in cytology, where it is used to aid pathologists in making a diagnosis. Although most notable for its use in the detection of cervical cancer in the Pap test or Pap smear, it is also used to stain non-gynecological specimen preparations from a variety of bodily secretions and from small needle biopsies of organs and tissues.

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