High-magnification image reconstruction from partial views

Paolo Virgili; Giovanni Venturi; Andrea Crovetto; Anna Maria Casali

doi:10.1117/12.175120

11 May 1994 High-magnification image reconstruction from partial views

Paolo Virgili, Giovanni Venturi, Andrea Crovetto, Anna Maria Casali

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175120
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

Observing specimen images at high magnification is often needed for histological analysis. Unfortunately, the higher the magnification, the more limited the field under observation. A single sample must be subdivided into several images, each representing a partial view, so that the global view and the geometrical correspondences among image components are missed. When using a conventional microscope, not equipped with a mechanical scanning mechanism, it is possible to overcome this drawback by exploited digital processing facilities. To this end, we have developed a method for high-magnification image reconstruction from partial views. Digital images of partial views from a single specimen are acquired and processed with image processing algorithms, in order to correct distortions and to eliminate overlapping. A global high-magnification image of the specimen is thus reconstructed.

Citation Download Citation

Paolo Virgili, Giovanni Venturi, Andrea Crovetto, and Anna Maria Casali "High-magnification image reconstruction from partial views", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175120

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available