Localization of tissues in high-resolution digital anatomic pathology images

Raja S. Alomari; Ron Allen; Bikash Sabata; Vipin Chaudhary

doi:10.1117/12.811430

3 March 2009 Localization of tissues in high-resolution digital anatomic pathology images

Raja S. Alomari, Ron Allen, Bikash Sabata, Vipin Chaudhary

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 726016 (2009) https://doi.org/10.1117/12.811430
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

High resolution digital pathology images have a wide range of variability in color, shape, size, number, appearance, location, and texture. The segmentation problem is challenging in this environment. We introduce a hybrid method that combines parametric machine learning with heuristic methods for feature extraction as well as pre- and post-processing steps for localizing diverse tissues in slide images. The method uses features such as color, intensity, texture, and spatial distribution. We use principal component analysis for feature reduction and train a two layer back propagation neural network (with one hidden layer). We perform image labeling at pixel-level and achieve higher than 96% automatic localization accuracy on 294 test images.

Citation Download Citation

Raja S. Alomari, Ron Allen, Bikash Sabata, and Vipin Chaudhary "Localization of tissues in high-resolution digital anatomic pathology images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726016 (3 March 2009); https://doi.org/10.1117/12.811430

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