Share Email Print

Proceedings Paper

Anatomical-functional image correlation problem: an interactive tool based on a hybrid method
Author(s): Patrizia Pisani; Riccardo Guzzardi; C. R. Bellina; O. Sorace
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The accurate localization of anatomical structures in functional images is a crucial point in positron emission tomography (PET) studies, due to the relatively poor spatial resolution in PET and to the strict dependence from the metabolic behavior of the used tracer. Until now, mainly two software approaches to the solution of the above problem have been used: the automatic match of PET images with a corresponding anatomical image, and the use of a computerized atlas of brain anatomical structures. The decision to adopt a `hybrid' method, allowing the users to rely on automatic image matching and, at the same time, also being able to intervene at any moment in the anatomic localization process, has led to the development of a user-friendly, interactive image processing tool, including a computer driven correlation process and a set of general-purpose image processing routines. The package, called CHIP (correlative hybrid image processing tool), has been implemented in C on a SUN 3/60 graphic workstation, with X11-R4 window system, using the XView toolkit (from OpenLook) to build the user-interface, and will soon be ported on a SUN SPARC-II workstation, with the aim of enhancing its performance. The correlation is implemented by extracting contours from the image obtained performing an anatomical scan (CT or MRI) of the patient, using a physical head-holder to match the PET slice, and transforming the contour image into a set of significant regions of interest (ROIs); these can undergo additional editing by the user to correct possible inaccuracies generated by the automated edge-finding process. CHIP is able to perform a lot of general-purpose image utilities, including: (1) Spatial filtering -- e.g., smoothing, edge crispening, median filtering, convolution with user-defined filter; and (2) Histogram package -- histogram drawing, automatic equalization, user-friendly manual histogram, rescaling and cutting. This package, together with the former, permits the user to enhance anatomical images. This is particularly important when using CT images that typically, especially in the brain cortex, show a contrast inadequate for detecting small cerebral structures.

Paper Details

Date Published: 1 June 1992
PDF: 5 pages
Proc. SPIE 1652, Medical Imaging VI: Image Processing, (1 June 1992); doi: 10.1117/12.59470
Show Author Affiliations
Patrizia Pisani, CNR Institute of Clinical Physiology (Italy)
Riccardo Guzzardi, CNR Institute of Clinical Physiology (Italy)
C. R. Bellina, Univ. of Pisa (Italy)
O. Sorace, CNR Institute of Clinical Physiology (Italy)

Published in SPIE Proceedings Vol. 1652:
Medical Imaging VI: Image Processing
Murray H. Loew, Editor(s)

© SPIE. Terms of Use
Back to Top