3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

3D and 4D Medical Image Registration Combined with Image Segmentation and Visualization

Guang Li (National Institutes of Health, USA), Deborah Citrin (National Cancer Institute, USA), Robert W. Miller (National Cancer Institute, USA), Kevin Camphausen (National Cancer Institute, USA), Boris Mueller (Memorial Sloan-Kettering Cancer Center, USA), Borys Mychalczak (Memorial Sloan-Kettering Cancer Center, USA) and Yulin Song (Memorial Sloan-Kettering Cancer Center, USA)
Copyright: © 2008 |Pages: 9
DOI: 10.4018/978-1-59904-889-5.ch001
OnDemand PDF Download:
$37.50

Abstract

Image registration, segmentation, and visualization are three major components of medical image processing. Three-dimensional (3D) digital medical images are three dimensionally reconstructed, often with minor artifacts, and with limited spatial resolution and gray scale, unlike common digital pictures. Because of these limitations, image filtering is often performed before the images are viewed and further processed (Behrenbruch, Petroudi, Bond, et al., 2004). Different 3D imaging modalities usually provide complementary medical information about patient anatomy or physiology. Four-dimensional (4D) medical imaging is an emerging technology that aims to represent patient motions over time. Image registration has become increasingly important in combining these 3D/4D images and providing comprehensive patient information for radiological diagnosis and treatment.

Key Terms in this Chapter

Image Registration: A process of transforming a set of patient images acquired at different times and/or with different modality into the same coordinate system, mapping corresponding voxels of these images in 3D space, based on the underlying anatomy or fiducial markers.

4D Medical Imaging: A process of acquiring multiple 3D images over time prospectively or retrospectively, so that patient motions and changes can be monitored and studied.

Image Processing: A computing technique in which various mathematical operations are applied to images for image enhancement, recognition, or interpretation, facilitating human efforts.

Imaging Modality: A type of medical imaging technique that utilizes a certain physical mechanism to detect patient internal signals that reflect either anatomical structures or physiological events.

3D Medical Imaging: A process of obtaining a 3D volumetric image composed of multiple 2D images, which are computer reconstructed using a mathematical “back-projection” operation to retrieve pixel data from projected image signals through a patient, detected via multichannel detector arrays around the patient.

Image Visualization: A process of converting (rendering) image pixel/voxel into 2D/3D graphical representation. Most computers support 8-bit (256) grayscale display, sufficient to human vision that can only resolve 32-64 grayscale. A common 12/16-bit (4096/65536 grayscales) medical image can be selectively displayed based on grayscale classification. Window width (display range in grayscale) and linear level function (center of the window width) are frequently used in adjusting display content.

Image Segmentation: A process in which an image is partitioned into multiple regions (sets of pixels/voxels in 2D/3D) based on a given criterion. These regions are nonoverlapping, homogeneous with respect to some characteristics such as intensity or texture. If the boundary constraint of the region is removed, the process is defined as classification.

Complete Chapter List

Search this Book:
Reset