Statistical approach to multiscale medial vision

James M. Coggins

doi:10.1117/12.130886

9 December 1992 Statistical approach to multiscale medial vision

James M. Coggins

Proceedings Volume 1768, Mathematical Methods in Medical Imaging; (1992) https://doi.org/10.1117/12.130886
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Definition of objects in medical images requires a multiscale approach because important structure appears across a wide range of scales. Object boundaries, when they are required, must be inferred from the multiscale structure of the image and a priori knowledge. For many objectbased tasks, explicit identification of boundaries is not necessary. Instead, it is possible to base object measures on medial axes and their radius functions obtained using statistical methods. A medial approach makes the easy decisions about the membership of pixels in the object first. The difficult decisions about the boundaries are made using a fuzzy measure of "objectness" that can account for edge uncertainty, partial volume effects, and a priori information. Objectness diffuses outward from the medial axis, and non-objectness diffuses inward from medial axes of surrounding regions. Their competition in boundary regions defines objectness even in the absence of an edge. The area of an object is the integral of objectness across space. Statistical pattern recognition methods (supervised and unsupervised classification; linear projections) are used to identiQy medial axes in a feature space defined by multiscale Gaussian filters. The pattern describing a pixel is formed from the response at that location and nearby locations to the filters. Approximations to derivatives of Gaussians are linear subspaces of this feature space.

Citation Download Citation

James M. Coggins "Statistical approach to multiscale medial vision", Proc. SPIE 1768, Mathematical Methods in Medical Imaging, (9 December 1992); https://doi.org/10.1117/12.130886

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