Feature Exploration using Local Frequency Distributions in Computed Tomography Data

Falk, Martin; Ljung, Patric; Lundström, Claes; Ynnerman, Anders; Hotz, Ingrid

Feature Exploration using Local Frequency Distributions in Computed Tomography Data

Files

013-024.pdf (8.68 MB)

Date

2020

Authors

Falk, Martin
Ljung, Patric
Lundström, Claes
Ynnerman, Anders
Hotz, Ingrid

Publisher

The Eurographics Association

Abstract

Frequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transfer function (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly in low-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to the area of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of local frequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allows us to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplify the exploration step. We propose three approaches for data exploration which we illustrate with several visualization cases highlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstrate the power of the method on selected datasets.

        @inproceedings{10.2312:vcbm.20201166
,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine
},
editor = {Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
},
title = {{Feature Exploration using Local Frequency Distributions in Computed Tomography Data
}},
author = {Falk, Martin and 
Ljung, Patric and 
Lundström, Claes and 
Ynnerman, Anders and 
Hotz, Ingrid
},
year = {2020
},
publisher = {The Eurographics Association
},
ISSN = {2070-5786
},
ISBN = {978-3-03868-109-0
},
DOI = {10.2312/vcbm.20201166
}
}