Mr. Taman Upadhaya Profile

Taman Upadhaya

Research scientist at Institut de recherche technologique B-Com

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 24 March 2016 Paper

Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices

Taman Upadhaya, Yannick Morvan, Eric Stindel, Pierre-Jean Le Reste, Mathieu Hatt

Proceedings Volume 9785, 97850W (2016) https://doi.org/10.1117/12.2217151

KEYWORDS: Feature extraction, Magnetic resonance imaging, Image classification, Oncology, Machine learning, Image processing, Image segmentation, Quantization, Medical imaging, Tumors, Scanners, Matrices, Brain

Read Abstract +

Heterogeneity image-derived features of Glioblastoma multiforme (GBM) tumors from multimodal MRI sequences may provide higher prognostic value than standard parameters used in routine clinical practice. We previously developed a framework for automatic extraction and combination of image-derived features (also called “Radiomics”) through support vector machines (SVM) for predictive model building. The results we obtained in a cohort of 40 GBM suggested these features could be used to identify patients with poorer outcome. However, extraction of these features is a delicate multi-step process and their values may therefore depend on the pre-processing of images. The original developed workflow included skull removal, bias homogeneity correction, and multimodal tumor segmentation, followed by textural features computation, and lastly ranking, selection and combination through a SVM-based classifier. The goal of the present work was to specifically investigate the potential benefit and respective impact of the addition of several MRI pre-processing steps (spatial resampling for isotropic voxels, intensities quantization and normalization) before textural features computation, on the resulting accuracy of the classifier. Eighteen patients datasets were also added for the present work (58 patients in total). A classification accuracy of 83% (sensitivity 79%, specificity 85%) was obtained using the original framework. The addition of the new pre-processing steps increased it to 93% (sensitivity 93%, specificity 93%) in identifying patients with poorer survival (below the median of 12 months). Among the three considered pre-processing steps, spatial resampling was found to have the most important impact. This shows the crucial importance of investigating appropriate image pre-processing steps to be used for methodologies based on textural features extraction in medical imaging.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years