3,181
Views
266
CrossRef citations to date
0
Altmetric
Original Article

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

, , , &
Pages 1012-1016 | Received 19 May 2010, Accepted 28 May 2010, Published online: 13 Sep 2010

References

  • Heron DE, Andrade RS, Beriwal S, Smith RP. PET/CT in radiation oncology – the impact on diagnosis, treatment planning, and assessment of treatment response. Am J Clin Oncol–Cancer Clin Trial 2008;31:352–62.
  • Paulino AC, Koshy M, Howell R, Schuster D, Davis LW. Comparison of CT- and FDG PET defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2005;61:1385–92.
  • Geets X, Tomsej M, Lee JA, Duprez T, Coche E, Cosnard G, . Adaptive biological image-guided imrt with anatomic and functional imaging in pharyngo-laryngeal tumors: Impact on target volume delineation and dose distribution using helical tomotherapy. Radiother Oncol 2007;85:105–15.
  • Devic S, Tomic N, Faria S, Dean G, Lisbona R, Parker W, . Impact of FDG PET on biological target volume (btv) definition for treatment planning for non-small cell lung cancer patients. Nucl Instrum Methods Phys Res Sect A 2007;571:89–92.
  • Riegel AC, Berson AM, Destian S, Ng T, Tena LB, Mitnick RJ, . Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion. Int J Radiat Oncol Biol Phys 2006;65:726–32.
  • Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rube C, . Comparison of different methods for delineation of FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 2005;46:1342–8.
  • Vauclin S, Doyeux K, Hapdey S, Edet-Sanson A, Vera P, Gardin I. Development of a generic thresholding algorithm for the delineation of FDG PET positive tissue: Application to the comparison of three thresholding models. Phys Med Biol 2009;54:6901–16.
  • Tylski P, Stute S, Grotus N, Doyeux K, Hapdey S, Gardin I, . Comparative assessment of methods for estimating tumor volume and standardized uptake value in f-18-fdg pet. J Nucl Med 2010;51:268–76.
  • Gonzalez RC, Woods RE. Digital Image Processing. 3rd Pearson Prentice Hall Upper Saddle River, New Jersey 07458: 2008.
  • Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;3:12.
  • Tang XO. Texture information in run-length matrices. IEEE Trans Image Process 1998;7:1602–9.
  • Sun CJ, Wee WG. Neighboring gray level dependence matrix for texture classification. Comput Vision Graph Image Process 1983;23:341–52.
  • Amadasun M, King R. Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 1989; 19:1264–74.
  • Xu Y, Sonka M, McLennan G, Guo JF, Hoffman EA. Mdct-based 3-d texture classification of emphysema and early smoking related lung pathologies. IEEE Trans Med Imaging 2006;25:464–75.
  • Bellotti R, De Carlo F, Tangaro S, Gargano G, Maggipinto G, Castellano M, . A completely automated cad system for mass detection in a large mammographic database. Med Phys 2006;33:3066–75.
  • Szekely N, Toth N, Pataki B. A hybrid system for detecting masses in mammographic images. IEEE Trans Instrum Meas 2006;55:944–52.
  • Assefa D, Keller H, Menard C, Laperriere N, Ferrari RJ, Yeung I. Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-flair MR images: A preliminary investigation in terms of identification and segmentation. Med Phys 2010;37: 1722–36.
  • Yu H, Caldwell C, Mah K, Mozeg D. Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging 2009;28(3):374–383.
  • Yu H, Caldwell C, Mah K, Poon I, Balogh J, MacKenzie R, . Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys 2009;75:618–25.
  • El Naqa I, Grigsby PW, Apte A, Kidd E, Donnelly E, Khullar D, . Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 2009;42:1162–71.
  • Thie JA. Understanding the standardized uptake value, its methods, and implications for usage. J Nucl Med 2004; 45(9):1431–4.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.