Tuning of optical coherence tomography texture features as a basis for tissue differentiation in glioblastoma samples

Jens Möller; Eveline Popanda; Iris Tischoff; Nuri H. Aydin; Hubert Welp; Carsten Brenner; Nils C. Gerhardt; Kirsten Schmieder; Dorothea Miller; Martin R. Hofmann

doi:10.1117/12.2609402

7 March 2022 Tuning of optical coherence tomography texture features as a basis for tissue differentiation in glioblastoma samples

Jens Möller, Eveline Popanda, Iris Tischoff, Nuri H. Aydin, Hubert Welp, Carsten Brenner, Nils C. Gerhardt, Kirsten Schmieder, Dorothea Miller, Martin R. Hofmann

Author Affiliations +

Proceedings Volume 11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; 119480L (2022) https://doi.org/10.1117/12.2609402
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

In this work we configure Haralick's texture parameter contrast to match the dimensions of characteristic structural features in ex vivo samples from glioblastoma (GBM) resection imaged with optical coherence tomography (OCT). The aim is to find and tune different texture features in a way that they enable the best possible basis for tissue classification using support vector machines (SVM). We used a sample collective including 18 tissue samples comprising 9 samples with at least 90% vital tumor and no healthy tissue, as well as 9 samples with 100% healthy tissue. All samples were imaged ex vivo immediately after resection. As a reference all samples were then examined professionally in the department of histopathology to determine tissue percentages. Based on the acquired 3D OCT images, texture features were extracted and optimized supported by the knowledge of medical professionals. Relations between the size of characteristic structures in healthy tissue as well as in GBM and different texture features were examined and evaluated. We focused on texture parameters as proposed by Haralick, relying on gray-level co-occurrence matrices (GLCMs). The displacement vector for the determination of those GLCMs was matched with size and direction of the characteristic structural tissue features of healthy and tumorous tissue. The results serve as a starting point to optimize the classification process of GBM against healthy tissue using SVM.

Conference Presentation

Citation Download Citation

Jens Möller, Eveline Popanda, Iris Tischoff, Nuri H. Aydin, Hubert Welp, Carsten Brenner, Nils C. Gerhardt, Kirsten Schmieder, Dorothea Miller, and Martin R. Hofmann "Tuning of optical coherence tomography texture features as a basis for tissue differentiation in glioblastoma samples", Proc. SPIE 11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 119480L (7 March 2022); https://doi.org/10.1117/12.2609402

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