3D image interpolation based on directional coherence

Yongmei Wang, Zhunping Zhang, Baining Guo

Research output: Contribution to conferencePaper

Abstract

Image interpolation is of great importance in biomedical visualization and analysis. In this paper, we present a novel gray-level interpolation method called Directional Coherence Interpolation (DCI). The principle advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. Since the edges of the image and the contents of the objects are well preserved along the MCDs, DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary/surface. A number of experiments were performed on both synthetic and real medical images to evaluate the proposed approach. The experimental results show that in addition to the substantial improvement of visual effects (qualitative evaluation), the quantitative error measures of DCI are also better than the conventional gray level linear interpolation. Comparing with the shape-based interpolation scheme applied on gray-level images, DCI has much lower computation cost.

Original languageEnglish (US)
Pages195-202
Number of pages8
StatePublished - Dec 1 2001
Externally publishedYes
EventWorkshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001 - Kauai, HI, United States
Duration: Dec 9 2001Dec 10 2001

Other

OtherWorkshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001
CountryUnited States
CityKauai, HI
Period12/9/0112/10/01

Fingerprint

Image Interpolation
3D Image
Interpolate
Interpolation Method
Image Space
Linear Interpolation
Information Structure
Medical Image
Rendering
Smoothness
Visualization
Evaluate
Requirements

ASJC Scopus subject areas

  • Analysis

Cite this

Wang, Y., Zhang, Z., & Guo, B. (2001). 3D image interpolation based on directional coherence. 195-202. Paper presented at Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, Kauai, HI, United States.

3D image interpolation based on directional coherence. / Wang, Yongmei; Zhang, Zhunping; Guo, Baining.

2001. 195-202 Paper presented at Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, Kauai, HI, United States.

Research output: Contribution to conferencePaper

Wang, Y, Zhang, Z & Guo, B 2001, '3D image interpolation based on directional coherence', Paper presented at Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, Kauai, HI, United States, 12/9/01 - 12/10/01 pp. 195-202.
Wang Y, Zhang Z, Guo B. 3D image interpolation based on directional coherence. 2001. Paper presented at Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, Kauai, HI, United States.
Wang, Yongmei ; Zhang, Zhunping ; Guo, Baining. / 3D image interpolation based on directional coherence. Paper presented at Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, Kauai, HI, United States.8 p.
@conference{ad9a710e3889482a90460b6f58aea260,
title = "3D image interpolation based on directional coherence",
abstract = "Image interpolation is of great importance in biomedical visualization and analysis. In this paper, we present a novel gray-level interpolation method called Directional Coherence Interpolation (DCI). The principle advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. Since the edges of the image and the contents of the objects are well preserved along the MCDs, DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary/surface. A number of experiments were performed on both synthetic and real medical images to evaluate the proposed approach. The experimental results show that in addition to the substantial improvement of visual effects (qualitative evaluation), the quantitative error measures of DCI are also better than the conventional gray level linear interpolation. Comparing with the shape-based interpolation scheme applied on gray-level images, DCI has much lower computation cost.",
author = "Yongmei Wang and Zhunping Zhang and Baining Guo",
year = "2001",
month = "12",
day = "1",
language = "English (US)",
pages = "195--202",
note = "Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001 ; Conference date: 09-12-2001 Through 10-12-2001",

}

TY - CONF

T1 - 3D image interpolation based on directional coherence

AU - Wang, Yongmei

AU - Zhang, Zhunping

AU - Guo, Baining

PY - 2001/12/1

Y1 - 2001/12/1

N2 - Image interpolation is of great importance in biomedical visualization and analysis. In this paper, we present a novel gray-level interpolation method called Directional Coherence Interpolation (DCI). The principle advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. Since the edges of the image and the contents of the objects are well preserved along the MCDs, DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary/surface. A number of experiments were performed on both synthetic and real medical images to evaluate the proposed approach. The experimental results show that in addition to the substantial improvement of visual effects (qualitative evaluation), the quantitative error measures of DCI are also better than the conventional gray level linear interpolation. Comparing with the shape-based interpolation scheme applied on gray-level images, DCI has much lower computation cost.

AB - Image interpolation is of great importance in biomedical visualization and analysis. In this paper, we present a novel gray-level interpolation method called Directional Coherence Interpolation (DCI). The principle advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. Since the edges of the image and the contents of the objects are well preserved along the MCDs, DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary/surface. A number of experiments were performed on both synthetic and real medical images to evaluate the proposed approach. The experimental results show that in addition to the substantial improvement of visual effects (qualitative evaluation), the quantitative error measures of DCI are also better than the conventional gray level linear interpolation. Comparing with the shape-based interpolation scheme applied on gray-level images, DCI has much lower computation cost.

UR - http://www.scopus.com/inward/record.url?scp=0035704012&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035704012&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0035704012

SP - 195

EP - 202

ER -