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Introduction of heat map to fidelity assessment of compressed CT images
Author(s) -
Lee Hyunna,
Kim Bohyoung,
Kim Kil Joong,
Seo Jinwook,
Park Seongjin,
Shin YeongGil,
Lee Kyoung Ho
Publication year - 2011
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3611046
Subject(s) - artificial intelligence , computer science , data set , outlier , cluster analysis , pattern recognition (psychology) , computer vision , jpeg 2000 , dicom , fidelity , image processing , image compression , image (mathematics) , telecommunications
Purpose: This study aimed to introduce heat map, a graphical data presentation method widely used in gene expression experiments, to the presentation and interpretation of image fidelity assessment data of compressed computed tomography (CT) images. Methods: The authors used actual assessment data that consisted of five radiologists' responses to 720 computed tomography images compressed using both Joint Photographic Experts Group 2000 (JPEG2000) 2D and JPEG2000 3D compressions. They additionally created data of two artificial radiologists, which were generated by partly modifying the data from two human radiologists. Results: For each compression, the entire data set, including the variations among radiologists and among images, could be compacted into a small color‐coded grid matrix of the heat map. A difference heat map depicted the advantage of 3D compression over 2D compression. Dendrograms showing hierarchical agglomerative clustering results were added to the heat maps to illustrate the similarities in the data patterns among radiologists and among images. The dendrograms were used to identify two artificial radiologists as outliers, whose data were created by partly modifying the responses of two human radiologists. Conclusions: The heat map can illustrate a quick visual extract of the overall data as well as the entirety of large complex data in a compact space while visualizing the variations among observers and among images. The heat map with the dendrograms can be used to identify outliers or to classify observers and images based on the degree of similarity in the response patterns.