A vector representation of local image contrast patterns for lesion classification

Weiguo Cao; Marc J. Pomeroy; Yongfeng Gao; Perry J. Pickhardt; Almas F. Abbasi; Jela Bandovic; Zhengrong Liang

doi:10.1117/12.2612939

4 April 2022 A vector representation of local image contrast patterns for lesion classification

Weiguo Cao, Marc J. Pomeroy, Yongfeng Gao, Perry J. Pickhardt, Almas F. Abbasi, Jela Bandovic, Zhengrong Liang

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120332L (2022) https://doi.org/10.1117/12.2612939
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Quantitative description of lesion image heterogeneity is a major task for computer-aided diagnosis of lesions, and it has remained a challenging task because the heterogeneity is associated with local image contrast patterns of each voxel. This work explores a novel vector representation of the local image contrast patterns of each voxel and learns the features from the local vector field across all voxels in the lesion volume. We generate a matrix from the first ring of surrounding voxels from each voxel in the image and perform a Karhunen-Loève transformation on this matrix. Using the eigenvectors associated with the largest three eigenvalues, we then generate a series of textures based on a vector representation of this matrix. Using an in-house dataset, experiments were performed to classify colorectal polyps using the learnt features and a Random Forest classifier to differentiate malignant from benign lesions. The outcomes show dramatic improvement for the lesion classification compared to seven existing classification methods (e.g. LBP, Haralick, VGG16), which learn the features from the original intensity image.

Citation Download Citation

Weiguo Cao, Marc J. Pomeroy, Yongfeng Gao, Perry J. Pickhardt, Almas F. Abbasi, Jela Bandovic, and Zhengrong Liang "A vector representation of local image contrast patterns for lesion classification", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332L (4 April 2022); https://doi.org/10.1117/12.2612939

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