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Due to the high volume of data generated, automatic image analysis is a necessity. [ 7 ] When positive and negative controls are available, the problem can be approached as a classification problem and the same techniques of feature computation and classification that are used for subcellular location analysis can be applied.
Shape analysis recently become of increasing interest to the medical community due to its potential to precisely locate morphological changes between different populations of structures, i.e. healthy vs pathological, female vs male, young vs elderly. Shape Analysis includes two main steps: shape correspondence and statistical analysis.
Modern image analysis systems can improve an observer's accuracy, objectivity, or speed. Image analysis is important for both diagnostics and research. Some examples are: high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics) morphometrics
Prostista taxonomy vs. phylogeny - This diagram shows the phylogeny of eukaryotes based on some recent analyses superimposed over the current kingdom and subkingdom-level taxonomy of protists. The purpose of the image is to demonstrate the paraphyly of most protist groupings, particularly those belonging to kingdom Protozoa: subkingdom Eozoa.
Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. [1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face .
The science of classification, in biology the arrangement of organisms into a classification [4] "The science of classification as applied to living organisms, including the study of means of formation of species, etc." [5] "The analysis of an organism's characteristics for the purpose of classification" [6]
[9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.
Machine learning can aid in analysis, and has been applied to expression pattern identification, classification, and genetic network induction. [2] A DNA-microarray analysis of Burkitt's lymphoma and diffuse large B-cell lymphoma (DLBCL), which differences in gene expression patterns