Segmentation and 3d visualization of high-resolution human brain cryosections

Ikuko Takanashi; Eric Lum; Kwan-Liu Ma; Joerg Meyer; Bernd Hamann; Arthur J. Olson

doi:10.1117/12.458810

Segmentation and 3d visualization of high-resolution human brain cryosections

Ikuko Takanashi, Eric Lum, Kwan-Liu Ma, Joerg Meyer, Bernd Hamann, Arthur J. Olson

Computer Science

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

We present a semi-automatic technique for segmenting a large cryo-sliced human brain data set that contains 753 high-resolution RGB color images. This human brain data set presents a number of unique challenges to segmentation and visualization due to its size (over 7 GB) as well as the fact that each image not only shows the current slice of the brain but also unsliced "deeper layers" of the brain. These challenges are not present in traditional MRI and CT data sets. We have found that segmenting this data set can be made easier by using the YIQ color model and morphology. We have used a hardware-assisted interactive volume renderer to evaluate our segmentation results.

Original language	English (US)
Pages (from-to)	55-61
Number of pages	7
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4665
DOIs	https://doi.org/10.1117/12.458810
State	Published - Jan 1 2002

Keywords

Color models
Cryosections
Medical imaging
Morphological operations
Segmentation
Volume rendering

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Access to Document

10.1117/12.458810

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title = "Segmentation and 3d visualization of high-resolution human brain cryosections",

abstract = "We present a semi-automatic technique for segmenting a large cryo-sliced human brain data set that contains 753 high-resolution RGB color images. This human brain data set presents a number of unique challenges to segmentation and visualization due to its size (over 7 GB) as well as the fact that each image not only shows the current slice of the brain but also unsliced {"}deeper layers{"} of the brain. These challenges are not present in traditional MRI and CT data sets. We have found that segmenting this data set can be made easier by using the YIQ color model and morphology. We have used a hardware-assisted interactive volume renderer to evaluate our segmentation results.",

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AU - Lum, Eric

AU - Ma, Kwan-Liu

AU - Meyer, Joerg

AU - Hamann, Bernd

AU - Olson, Arthur J.

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