S
Satrajit Basu
Researcher at University of South Florida
Publications - 6
Citations - 1994
Satrajit Basu is an academic researcher from University of South Florida. The author has contributed to research in topics: Feature (computer vision) & Image segmentation. The author has an hindex of 6, co-authored 6 publications receiving 1533 citations.
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Journal ArticleDOI
Radiomics: the process and the challenges
Virendra Kumar,Yuhua Gu,Satrajit Basu,Anders Berglund,Steven A. Eschrich,Matthew B. Schabath,Kenneth M. Forster,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Andre Dekker,David Fenstermacher,Dmitry B. Goldgof,Lawrence O. Hall,Philippe Lambin,Yoganand Balagurunathan,Robert A. Gatenby,Robert J. Gillies +16 more
TL;DR: "Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging, leading to a very large potential subject pool.
Journal ArticleDOI
Test–Retest Reproducibility Analysis of Lung CT Image Features
Yoganand Balagurunathan,Virendra Kumar,Yuhua Gu,Jongphil Kim,Hua Wang,Ying Liu,Dmitry B. Goldgof,Lawrence O. Hall,Rene Korn,Binsheng Zhao,Lawrence H. Schwartz,Satrajit Basu,Steven A. Eschrich,Robert A. Gatenby,Robert J. Gillies +14 more
TL;DR: Test–retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range, making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.
Journal ArticleDOI
Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
Samuel H. Hawkins,John N. Korecki,Yoganand Balagurunathan,Yuhua Gu,Virendra Kumar,Satrajit Basu,Lawrence O. Hall,Dmitry B. Goldgof,Robert A. Gatenby,Robert J. Gillies +9 more
TL;DR: Focusing on cases of the adenocarcinoma nonsmall cell lung cancer tumor subtype from a larger data set, it is shown that classifiers can be built to predict survival time, the first known result to make such predictions from CT scans of lung cancer.
Proceedings ArticleDOI
Developing a classifier model for lung tumors in CT-scan images
Satrajit Basu,Lawrence O. Hall,Dmitry B. Goldgof,Yuhua Gu,Virendra Kumar,Jung Choi,Robert J. Gillies,Robert A. Gatenby +7 more
TL;DR: Results show that over the large feature space for both 2D and 3D features it is possible to recognize tumor classes with about 68% accuracy, showing new features may be of help.
Developing Predictive Models for Lung Tumor Analysis
TL;DR: In a first of its kind investigation, a large group of 2D and 3D image features, which were hypothesized to be useful, are evaluated for effectiveness in classifying the tumors and it is shown that over the large feature space for both 1D and 2D features it is possible to predict tumor classes with over 63% accuracy, showing new features may be of help.