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((short description|Samples within a data set identified for a particular purpose))
((refimprove|date=September 2012))
[[File:Markov Inequality.svg|250px|thumbnail|right|The region of interest for which [[Markov's inequality]] gives a lower bound.]]
A '''region of interest''' (often abbreviated '''ROI''') is a sample within a [[data set]] identified for a particular purpose.<ref name="Brinkmann1999">((cite book|author=Ron Brinkmann|title=The Art and Science of Digital Compositing|url=https://archive.org/details/artscienceofdigi00brin|url-access=registration|year=1999|publisher=Morgan Kaufmann|isbn=978-0-12-133960-9|pages=[https://archive.org/details/artscienceofdigi00brin/page/184 184]))</ref> The concept of a ROI is commonly used in many application areas. For example, in [[medical imaging]], the boundaries of a tumor may be defined on an image or in a volume, for the purpose of measuring its size. The endocardial border may be defined on an image, perhaps during different phases of the cardiac cycle, for example, end-systole and end-diastole, for the purpose of assessing cardiac function. In [[geographical information system]]s (GIS), a ROI can be taken literally as a polygonal selection from a 2D map. In [[computer vision]] and [[optical character recognition]], the ROI defines the [[edge detection|borders]] of an object under consideration. In many applications, symbolic (textual) labels are added to a ROI, to describe its content in a compact manner. Within a ROI may lie individual ''[[point of interest|points of interest]]'' (POIs).

==Examples of regions of interest==
* 1D dataset: a time or frequency interval on a waveform
* 2D dataset: the boundaries of an object on an image
* 3D dataset: the contours or surfaces outlining an object (sometimes known as the '''Volume of Interest''' ('''VOI''')) in a volume
* 4D dataset: the outline of an object at or during a particular time interval in a time-volume

A ROI is a form of [[Annotation]], often associated with categorical or quantitative information (e.g., measurements like volume or mean intensity), expressed as text or in a structured form.

There are three fundamentally different means of encoding a ROI:
* As an integral part of the sample data set, with a unique or masking value that may or may not be outside the normal range of normally occurring values and which tags individual data cells
* As separate, purely graphic information, such as with vector or bitmap (rasterized) drawing elements, perhaps with some accompanying plain [[unstructured data|(unstructured)]] text in the format of the data itself
* As a separate structured [[semantic]] information (such as coded [[value type]]s) with a set of [[spatial coordinates|spatial]] and/or temporal coordinates

==Medical imaging==
[[File:Mammograms MED-SEG.jpg|thumb|right|250px|The left image shows an original mammogram before MED-SEG processing. The image on the right, with region of interest (white) labeled, shows a mammogram after MED-SEG processing.]]
Medical imaging standards such as [[DICOM]] provide general and application-specific mechanisms to support various use-cases.

For DICOM images (two or more dimensions):
* Burned in graphics and text may occur within the normal [[pixel]] value range (e.g., as the maximum white value) (deprecated)
* Bitmap (rasterized) overlay graphics and text may be present in unused high bits of the pixel data or in a separate attribute (deprecated)
* Vector graphics may be encoded in separate image attributes as curves (deprecated)
* Unstructured vector graphics and text as well as bitmap (rasterized) overlay graphics may be encoded in a separate object as a presentation state that references the image object to which it is to be applied
* Structured data may be encoded in a separate object as a structured report in the form of a tree of name-value pairs of coded or text concepts possibly associated with derived quantitative information can reference spatial and/or temporal coordinates that in turn reference the image objects to which they apply
* Reference locations may be encoded as [[fiducial marker|fiducial]]s in the form of spatial coordinates with an associated coded purpose, either as pixel coordinates by reference to specific images or as coordinates in a named patient-relative 3D Cartesian space
* Pixels (possibly non-contiguous) may be classified into [[image segment|segments]] encoded in a segmentation object as either binary or probabilistic values in a raster (which is not required to have the same spatial sampling or extent as the images from which the segmentation was derived); these are usually referenced by other objects containing structured content (structured reports)

For DICOM radiotherapy:
* Contours of objects may be defined as structure sets, either as pixel coordinates by reference to specific images or as coordinates in a named patient-relative 3D Cartesian space (these are also used for non-RT applications)

For DICOM time-based waveforms:
* Burned in values may occur with the waveform (deprecated)
* Annotations may be encoded in a separate attribute can select multiple time points or a range of time points, either by sample number or specified time
* Structured data may be encoded in a separate object as a structured report in the form of a tree of name-value pairs of coded or text concepts possibly associated with derived quantitative information can reference temporal coordinates that in turn reference the waveform objects to which they apply

[[HL7]] [[Clinical Document Architecture]] also has a subset of mechanisms similar to (and intended to be compatible with) DICOM for referencing image-related spatial coordinates as observations; it allows for a circle, ellipse, [[polyline]] or point to be defined as integer pixel-relative coordinates referencing an external multi-media image object, which may be of a consumer rather than medical image format (e.g., a [[GIF]], [[Portable Network Graphics|PNG]] or [[JPEG]]).

==Document analysis systems==
In [[Optical Character Recognition]] (OCR) and [[Document Layout Analysis]], regions of interest (ROIs) hierarchically encompass pages, text or graphical blocks, down to individual line-strip images, word and character image boxes. The de facto standard in archives and libraries is the tuplet {image_file, xml_file}, usually in the form of a *.tif file and its accompanying *.xml file.

==Other 2D applications==
As far as non-medical standards are concerned, in addition to the purely graphic markup languages (such as [[PostScript]] or [[PDF]]) and vector graphic (such as [[Scalable Vector Graphics|SVG]]) and 3D (such as [[VRML]]) drawing file formats that are widely available, and which carry no specific ROI semantics, some standards such as [[JPEG 2000]] specifically provide mechanisms to label and/or compress to a different degree of fidelity, what they refer to as regions of interest.

==References==
((reflist))

[[Category:Medical imaging]]
[[Category:Optical character recognition]]
[[Category:Geographic information systems]]

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