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A framework for automated tumor detection in thoracic FDG pet images using texture-based features

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7 Author(s)
Saradhi, G.V. ; Comput. & Decision Sci. Lab., GE Global Res., Bangalore, India ; Gopalakrishnan, G. ; Roy, A.S. ; Mullick, R.
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This paper proposes a novel framework for tumor detection in Positron Emission Tomography (PET) images. A set of 8 second-order texture features obtained from the gray level co-occurrence matrix (GLCM) across 26 offsets, together with uptake value was used to construct a feature vector at each voxel in the data. Volume of Interest (VOI) samples from 42 images (7 patients with 6 gates each), marked by a radiologist, representing 5 distinct anatomy types and pathology were used to train a logit boost classifier. A ten-fold cross-validation showed a true positive rate of 96%and a false positive rate of 8% for tumor classification. The test dataset consisted of 50 times 50 times 40 representative VOIs from gated PET images of 3 patients. The classifier was run on the test data, followed by an SUV-based thresholding and elimination of noise using connected component analysis. The method detected 10/12 (83%) tumors while detecting an average of 20 false positive structures.

Published in:

Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on

Date of Conference:

June 28 2009-July 1 2009