Shape-based interpolation of tree-like structures in three-dimensional images

W. E. Higgins, C. Morice, E. L. Ritman

Research output: Contribution to journalArticle

71 Citations (Scopus)

Abstract

Three-dimensional (3-D) images obtainable from many medical-imaging scanners typically have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the `missing slices'. Unfortunately, linear interpolation and related schemes produce a 3-D image having blurred object structures. Thus, when objects are extracted and displayed from the interpolated image, the objects often exhibit a blocky and generally unsatisfactory appearance. This problem is particularly acute for thin tree-like structures such as the coronary arteries. Recently workers in the field have proposed a strategy referred to as shape-based interpolation that offers improvement to linear interpolation. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image. Then, the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. These techniques however, do not use the original gray-scale information and have difficulties with images containing tree-like structures, such as the coronary arteries. We describe two shape-based interpolation methods that generate improved results from tree-like structures. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds upon the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.

Original languageEnglish (US)
Pages (from-to)439-450
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume12
Issue number3
DOIs
StatePublished - Sep 1993

Fingerprint

Three-Dimensional Imaging
Interpolation
Coronary Vessels
Medical imaging
Diagnostic Imaging
Health Personnel

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Shape-based interpolation of tree-like structures in three-dimensional images. / Higgins, W. E.; Morice, C.; Ritman, E. L.

In: IEEE Transactions on Medical Imaging, Vol. 12, No. 3, 09.1993, p. 439-450.

Research output: Contribution to journalArticle

Higgins, W. E. ; Morice, C. ; Ritman, E. L. / Shape-based interpolation of tree-like structures in three-dimensional images. In: IEEE Transactions on Medical Imaging. 1993 ; Vol. 12, No. 3. pp. 439-450.
@article{5e9bec7c8793418d9343d82755b1911c,
title = "Shape-based interpolation of tree-like structures in three-dimensional images",
abstract = "Three-dimensional (3-D) images obtainable from many medical-imaging scanners typically have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the `missing slices'. Unfortunately, linear interpolation and related schemes produce a 3-D image having blurred object structures. Thus, when objects are extracted and displayed from the interpolated image, the objects often exhibit a blocky and generally unsatisfactory appearance. This problem is particularly acute for thin tree-like structures such as the coronary arteries. Recently workers in the field have proposed a strategy referred to as shape-based interpolation that offers improvement to linear interpolation. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image. Then, the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. These techniques however, do not use the original gray-scale information and have difficulties with images containing tree-like structures, such as the coronary arteries. We describe two shape-based interpolation methods that generate improved results from tree-like structures. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds upon the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.",
author = "Higgins, {W. E.} and C. Morice and Ritman, {E. L.}",
year = "1993",
month = "9",
doi = "10.1109/42.241871",
language = "English (US)",
volume = "12",
pages = "439--450",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Shape-based interpolation of tree-like structures in three-dimensional images

AU - Higgins, W. E.

AU - Morice, C.

AU - Ritman, E. L.

PY - 1993/9

Y1 - 1993/9

N2 - Three-dimensional (3-D) images obtainable from many medical-imaging scanners typically have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the `missing slices'. Unfortunately, linear interpolation and related schemes produce a 3-D image having blurred object structures. Thus, when objects are extracted and displayed from the interpolated image, the objects often exhibit a blocky and generally unsatisfactory appearance. This problem is particularly acute for thin tree-like structures such as the coronary arteries. Recently workers in the field have proposed a strategy referred to as shape-based interpolation that offers improvement to linear interpolation. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image. Then, the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. These techniques however, do not use the original gray-scale information and have difficulties with images containing tree-like structures, such as the coronary arteries. We describe two shape-based interpolation methods that generate improved results from tree-like structures. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds upon the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.

AB - Three-dimensional (3-D) images obtainable from many medical-imaging scanners typically have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the `missing slices'. Unfortunately, linear interpolation and related schemes produce a 3-D image having blurred object structures. Thus, when objects are extracted and displayed from the interpolated image, the objects often exhibit a blocky and generally unsatisfactory appearance. This problem is particularly acute for thin tree-like structures such as the coronary arteries. Recently workers in the field have proposed a strategy referred to as shape-based interpolation that offers improvement to linear interpolation. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image. Then, the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. These techniques however, do not use the original gray-scale information and have difficulties with images containing tree-like structures, such as the coronary arteries. We describe two shape-based interpolation methods that generate improved results from tree-like structures. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds upon the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.

UR - http://www.scopus.com/inward/record.url?scp=0027664132&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027664132&partnerID=8YFLogxK

U2 - 10.1109/42.241871

DO - 10.1109/42.241871

M3 - Article

AN - SCOPUS:0027664132

VL - 12

SP - 439

EP - 450

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 3

ER -