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  • Budzevich, M  (2)
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  • 1
    Online Resource
    Online Resource
    SU‐E‐QI‐17: Dependence of 3D/4D PET Quantitative Image Features On Noise
    Wiley ; 2014
    In:  Medical Physics Vol. 41, No. 6Part22 ( 2014-06), p. 380-380
    Details
    In: Medical Physics, Wiley, Vol. 41, No. 6Part22 ( 2014-06), p. 380-380
    Abstract: Quantitative imaging is a fast evolving discipline where a large number of features are extracted from images; i.e., radiomics. Some features have been shown to have diagnostic, prognostic and predictive value. However, they are sensitive to acquisition and processing factors; e.g., noise. In this study noise was added to positron emission tomography (PET) images to determine how features were affected by noise. Methods: Three levels of Gaussian noise were added to 8 lung cancer patients PET images acquired in 3D mode (static) and using respiratory tracking (4D); for the latter images from one of 10 phases were used. A total of 62 features: 14 shape, 19 intensity (1stO), 18 GLCM textures (2ndO; from grey level co‐occurrence matrices) and 11 RLM textures (2ndO; from run‐length matrices) features were extracted from segmented tumors. Dimensions of GLCM were 256×256, calculated using 3D images with a step size of 1 voxel in 13 directions. Grey levels were binned into 256 levels for RLM and features were calculated in all 13 directions. Results: Feature variation generally increased with noise. Shape features were the most stable while RLM were the most unstable. Intensity and GLCM features performed well; the latter being more robust. The most stable 1stO features were compactness, maximum and minimum length, standard deviation, root‐mean‐squared, I30, V10‐V90, and entropy. The most stable 2ndO features were entropy, sum‐average, sum‐entropy, difference‐average, difference‐variance, difference‐entropy, information‐correlation‐2, short‐run‐emphasis, long‐run‐emphasis, and run‐percentage. In general, features computed from images from one of the phases of 4D scans were more stable than from 3D scans. Conclusion: This study shows the need to characterize image features carefully before they are used in research and medical applications. It also shows that the performance of features, and thereby feature selection, may be assessed in part by noise analysis.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Article
    DOI: 10.1118/1.4888997
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 1466421-5
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    Online Resource
  • 2
    Online Resource
    Online Resource
    SU‐E‐QI‐16: Reproducibility of Computed Tomography Quantitative Structural Features Using the FDA Thoracic Phantom Image Database
    Wiley ; 2014
    In:  Medical Physics Vol. 41, No. 6Part22 ( 2014-06), p. 380-380
    Details
    In: Medical Physics, Wiley, Vol. 41, No. 6Part22 ( 2014-06), p. 380-380
    Abstract: To assess the reproducibility of quantitative structural features using images from the computed tomography thoracic FDA phantom database under different scanning conditions. Methods: Development of quantitative image features to describe lesion shape and size, beyond conventional RECIST measures, is an evolving area of research in need of benchmarking standards. Gavrielides et al. (2010) scanned a FDA‐developed thoracic phantom with nodules of various Hounsfield units (HU) values, shapes and sizes close to vascular structures using several scanners and varying scanning conditions/parameters; these images are in the public domain. We tested six structural features, namely, Convexity, Perimeter, Major Axis, Minor Axis, Extent Mean and Eccentricity, to characterize lung nodules. Convexity measures lesion irregularity referenced to a convex surface. Previously, we showed it to have prognostic value in lung adenocarcinoma. The above metrics and RECIST measures were evaluated on three spiculated (8mm/‐300HU, 12mm/+30HU and 15mm/+30HU) and two non‐spiculated (8mm/+100HU and 10mm/+100HU) nodules (from layout 2) imaged at three different mAs values: 25, 100 and 200 mAs; on a Phillips scanner (16‐slice Mx8000‐IDT; 3mm slice thickness). The nodules were segmented semi‐automatically using a commercial software tool; the same HU range was used for all nodules. Results: Analysis showed convexity having the lowest maximum coefficient of variation (MCV): 1.1% and 0.6% for spiculated and non‐spiculated nodules, respectively, much lower compared to RECIST Major and Minor axes whose MCV were 10.1% and 13.4% for spiculated, and 1.9% and 2.3% for non‐spiculated nodules, respectively, across the various mAs. MCVs were consistently larger for speculated nodules. In general, the dependence of structural features on mAs (noise) was low. Conclusion: The FDA phantom CT database may be used for benchmarking of structural features for various scanners and scanning conditions; we used only a small fraction of available data. Our feature convexity outperformed other structural features including RECIST measures.
    Type of Medium: Online Resource
    ISSN: 0094-2405 , 2473-4209
    URL: Article
    DOI: 10.1118/1.4888996
    Language: English
    Publisher: Wiley
    Publication Date: 2014
    detail.hit.zdb_id: 1466421-5
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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