Paper Info
Title
Stability Assessment Of First Order Statistics Features Computed On Adc Maps In Soft-Tissue Sarcoma
Abstract
Radiomics extracts a large number of features from medical images to perform a quantitative characterization. Aim of this study was to assess radiomic features stability and relevance. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of 18 patients diagnosed with soft-tissue sarcomas (STSs). Thirty-seven intensity-based features were computed on the regions of interest (ROIs). First, ROIs of the images were subjected to translations and rotations in specific ranges. The 37 features computed on the original and transformed ROIs were compared in terms of percentage of variations. The intra-class correlation coefficient (ICC) was computed. To be accepted, a feature should satisfy the following conditions: the ICC after a minimum entity transformation is > 0.6 and the ICC after a maximum entity translation is < 0.4. In total, 31 features out of 37 were accepted by the algorithm. This stability analysis can be used as a first step in the features selection process.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8036899
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Biomedical engineering,Computer vision,Correlation coefficient,Effective diffusion coefficient,Pattern recognition,Computer science,First order,Stability assessment,Artificial intelligence,Radiomics,Soft tissue sarcoma,Magnetic resonance imaging
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Marco Bologna100.34
Eros Montin202.03
Valentina D. A. Corino33012.91
Luca T. Mainardi410626.02