Vocabulary for Chapter 11

Last updated on Apr 24, 2020 3 min read vocabulary, Chapter 11

Chapter 11 is focused on learning how to read, write, and manipulate images in R. The first sections are helping the reader understand when to apply different filters and transformations to an image and why it is necessary. It then touches on segmentation and feature extraction, two components that are utilized to simplify an image for machine learning and recognition. Finally, statistal methods are introduced to analyze spacial distributions and spatial point process is introduced on a basic level.

The vocabulary words for Chapter 11 are:


segmentation	partitioning an image to assign a label to every pixel or group of pixels with similar characteristics
slot	a part, element, or “property” of an object in the context of object-oriented programming in R
classification	the process of grouping observations in a dataset by their similarities in terms of measured characteristics
feature extraction	the process of building derived values to describe observations or features from the initial set of measured data, with the aim of creating a new set of characteristics that is informative.
spatial point process	mechanism that generates a random collection of coordinates or points randomly located along an underlying mathematical space. There is at most one point observed at any location.
Poisson process	mechanism that generates instantaneous events (in time and/or space) based on the Poisson distribution
Ripley’s K function	a descriptive statistic for detecting the deviations from spatial homogeneity that can help determine if points are random, dispersed, or clustered
pair correlation function	a description of how the point density varies as a function of distance from the point of reference
spatial transformation	changes to a coordinate system that provides a new approach to defining variation of material parameters
linear filter	a tool for refining an image such that the output pixel is a combination of the time-varying input pixels subject to the constraint of linearity
dynamic range	the ratio or logarithmic value of the difference between the largest and smallest values
noise reduction	the process of removing the undesirable variation in image pixelation
adaptive thresholding	segmenting an image using smaller regions that are defined by the range of local intensity values
binary image	an image consisting of pixels that can only have one of exactly two values, usually black and white
morphological operation	image processing in which each individual pixel in the image is adjusted based on the other pixels in the neighborhood
Voronoi tessellation	partitioning an image plane into regions closest to each set of points. Line segments are formed equidistant to the two nearest points
convex hull	the smallest polygon that encloses all of the points of interest in a set
spatial dependence	the propensity for nearby points to influence each other and possess similar attributes
virion	infectious nucleic acid surrounded by a protective protein capsid
actin	globular proteins that form the microfilaments essential for cell mobility and division
macrophages	immune cells responsible for engulfing potential pathogens and other lymphatic particles
dendritic cells	immune cells responsible for processing foreign material and presenting it to other cells in the immune system
light sheet microscopy	a method that illuminates a specimen in a single plane and detected from the perpendicular direction