Unsupervised detection of density changes through principal component analysis for lung lesion classification-Reference-Cited by-同舟云学术

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Unsupervised detection of density changes through principal component analysis for lung lesion classification

Published:2017-02-15 Issue:18 Volume:76 Page:18929-18954
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Barros Netto Stelmo Magalhães,Silva Aristófanes Corrêa,Cardoso de Paiva Anselmo,Nunes Rodolfo Acatauassú,Gattass Marcelo

Funder

Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/article/10.1007/s11042-017-4414-6/fulltext.html

Reference39 articles.

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2. Bashir U, Siddique M, McLean E, Goh V, Cook G (2016) Imaging heterogeneity in lung cancer: Techniques, applications, and challenges. Am J Roentgenol 207(3):534–543. doi: 10.2214/AJR.15.15864

3. Böttcher J, Renz DM, Zahm D-M, Pfeil A, Fallenberg EM, Streitparth F, Maurer MH, Hamm B, Engelken FJ (2014) Response to neoadjuvant treatment of invasive ductal breast carcinomas including outcome evaluation: Mri analysis by an automatic cad system in comparison to visual evaluation. Acta Oncol 53(6):759–768. doi: 10.3109/0284186X.2013.852688 . PMID: 24299492

4. Chen B, Kitasaka T, Honma H, Takabatake H, Mori M, Natori H, Mori K Automatic Segmentation of Solitary Pulmonary Nodules Based on Local Intensity Structure Analysis and 3D Neighborhood Features in 3D Chest CT Images. doi: 10.1117/12.911782

5. Chen Y-W, Lin C-J (2006) Combining SVMs with Various Feature Selection Strategies. In: Guyon I, Nikravesh M, Gunn S, Zadeh LA (eds) Feature Extraction: Foundations and Applications. doi: 10.1007/978-3-540-35488-813 . Springer, Berlin, Heidelberg, pp 315–324

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