StainingStaining 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 analysisImage 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 detectionObject 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 stainingEndospore 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 stainPapanicolaou 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.
Corner detectionCorner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, , video tracking, image mosaicing, panorama stitching, 3D reconstruction and object recognition. Corner detection overlaps with the topic of interest point detection. A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point.
Silver stainingIn pathology, silver staining is the use of silver to selectively alter the appearance of a target in microscopy of histological sections; in temperature gradient gel electrophoresis; and in polyacrylamide gels. In traditional stained glass, silver stain is a technique to produce yellow to orange or brown shades (or green on a blue glass base), by adding a mixture containing silver compounds (notably silver nitrate), and firing lightly. It was introduced soon after 1800, and is the "stain" in the term "stained glass".
Vital stainA vital stain in a casual usage may mean a stain that can be applied on living cells without killing them. Vital stains have been useful for diagnostic and surgical techniques in a variety of medical specialties. In supravital staining, living cells have been removed from an organism, whereas intravital staining is done by injecting or otherwise introducing the stain into the body. The term vital stain is used by some authors to refer to an intravital stain, and by others interchangeably with a supravital stain, the core concept being that the cell being examined is still alive.
Face detectionFace detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.
Edge detectionEdge detection includes a variety of mathematical methods that aim at identifying edges, curves in a at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in , machine vision and computer vision, particularly in the areas of feature detection and feature extraction.
Medical image computingMedical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.
Image segmentationIn 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.
Romanowsky stainRomanowsky staining, also known as Romanowsky–Giemsa staining, is a prototypical staining technique that was the forerunner of several distinct but similar stains widely used in hematology (the study of blood) and cytopathology (the study of diseased cells). Romanowsky-type stains are used to differentiate cells for microscopic examination in pathological specimens, especially blood and bone marrow films, and to detect parasites such as malaria within the blood.
Hoechst stainHoechst stains are part of a family of blue fluorescent dyes used to stain DNA. These bis-benzimides were originally developed by Hoechst AG, which numbered all their compounds so that the dye Hoechst 33342 is the 33,342nd compound made by the company. There are three related Hoechst stains: Hoechst 33258, Hoechst 33342, and Hoechst 34580. The dyes Hoechst 33258 and Hoechst 33342 are the ones most commonly used and they have similar excitation–emission spectra.
Digital image processingDigital 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.
