Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Computer Assisted Radiology and Surgery
Erschienen in: International Journal of Computer Assisted Radiology and Surgery 4/2013

01.07.2013 | Original Article

Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images

verfasst von: Konstantinos P. Sidiropoulos, Spiros A. Kostopoulos, Dimitris T. Glotsos, Emmanouil I. Athanasiadis, Nikos D. Dimitropoulos, John T. Stonham, Dionisis A. Cavouras

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 4/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Purpose To improve the computer-aided diagnosis of breast lesions, by designing a pattern recognition system (PR-system) on commercial graphics processing unit (GPU) cards using parallel programming and textural information from multimodality imaging.
Material and methods Patients with histologically verified breast lesions underwent both ultrasound (US) and digital mammography (DM), lesions were outlined on the images by an experienced radiologist, and textural features were calculated. The PR-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA’s GPU cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the probabilistic neural network classifier, and its performance was evaluated by a re-substitution method, for estimating the system’s highest accuracy, and by the external cross-validation method, for assessing the PR-system’s unbiased accuracy to new, “unseen” by the system, data.
Results Classification accuracies for discriminating malignant from benign lesions were as follows: 85.5 % using US-features alone, 82.3 % employing DM features alone, and 93.5 % combining US and DM features. Mean accuracy to new “unseen” data for the combined US and DM features was 81 %. Those classification accuracies were about 10 % higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster.
Conclusion The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.
Vorheriger Artikel Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms
Nächster Artikel An evaluation of image descriptors combined with clinical data for breast cancer diagnosis

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Anhänge
Nur mit Berechtigung zugänglich
Literatur
1.
Laine HR, Tukeva T, Mikkola P et al (1996) Assessment of mammography and ultrasound examination in the diagnosis of breast cancer. Eur J Ultrasound 3(1):9–14CrossRef
2.
Tang J, Rangayyan RM, Xu J et al (2009) Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13(2):236–251PubMedCrossRef
3.
Humphrey LL, Helfand M, Chan BK et al (2002) Breast cancer screening: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 137((5 Part 1)):347–360PubMedCrossRef
4.
Majid AS, de Paredes ES, Doherty RD et al (2003) Missed breast carcinoma: pitfalls and pearls. Radiographics 23(4):881–895PubMedCrossRef
5.
Wang X, Lederman D, Tan J et al (2010) Computerized detection of breast tissue asymmetry depicted on bilateral mammograms: a preliminary study of breast risk stratification. Acad Radiol 17(10):1234–1241PubMedCrossRef
6.
Farber R (2011) CUDA application design and development. Morgan Kaufmann, Los Altos, CA
7.
Evers K (2001) Diagnostic breast imaging. Am J Roentgenol 177(5):1094CrossRef
8.
Harms SE (1999) Technical report of the international working group on breast MRI. J Magn Reson Imaging 10(6):979PubMedCrossRef
9.
Bird RE, Wallace TW, Yankaskas BC (1992) Analysis of cancers missed at screening mammography. Radiology 184(3):613–617PubMed
10.
Beam CA, Layde PM, Sullivan DC (1996) Variability in the interpretation of screening mammograms by US radiologists. Findings from a national sample. Arch Intern Med 156(2):209–213PubMedCrossRef
11.
Sardanelli F, Podo F, D’Agnolo G et al (2007) Multicenter comparative multimodality surveillance of women at genetic-familial high risk for breast cancer (HIBCRIT study): interim results. Radiology 242(3):698–715PubMedCrossRef
12.
Lee JM, Halpern EF, Rafferty EA et al (2009) Evaluating the correlation between film mammography and MRI for screening women with increased breast cancer risk. Acad Radiol 16(11):1323–1328PubMedCrossRef
13.
Houssami N, Ciatto S (2011) The evolving role of new imaging methods in breast screening. Prev Med 53(3):123–126PubMedCrossRef
14.
Malvindi MA, Greco A, Conversano F et al (2011) Magnetic/silica nanocomposites as dual-mode contrast agents for combined magnetic resonance imaging and ultrasonography. Adv Funct Mater 21(13):2548–2555CrossRef
15.
Soloperto G, Conversano F, Greco A et al (2012) Advanced spectral analyses for real-time automatic echographic tissue-typing of simulated tumor masses at different compression stages. IEEE Trans Ultrason Ferroelectr Freq Control 59(12):2692–2701PubMedCrossRef
16.
Yuan Y, Giger ML, Li H et al (2010) Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad Radiol 17(9):1158–1167PubMedCrossRef
17.
Drukker K, Horsch K, Giger ML (2005) Multimodality computerized diagnosis of breast lesions using mammography and sonography. Acad Radiol 12(8):970–979PubMedCrossRef
18.
Sahiner B, Chan HP, Hadjiiski LM et al (2009) Multi-modality CADx: ROC study of the effect on radiologists’ accuracy in characterizing breast masses on mammograms and 3D ultrasound images. Acad Radiol 16(7):810–818PubMedCrossRef
19.
Horsch K, Giger ML, Vyborny CJ et al (2006) Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. Radiology 240(2):357–368PubMedCrossRef
20.
Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press, San Diego
21.
Xu F, Mueller K (2007) Real-time 3D computed tomographic reconstruction using commodity graphics hardware. Phys Med Biol 52(12):3405–3419PubMedCrossRef
22.
Ruiz A, Sertel O, Ujaldon M et al (2009) Stroma classification for neuroblastoma on graphics processors. Int J Data Min Bioinform 3(3):280–298PubMedCrossRef
23.
Lapeer RJ, Shah SK, Rowland RS (2010) An optimised radial basis function algorithm for fast non-rigid registration of medical images. Comput Biol Med 40(1):1–7PubMedCrossRef
24.
Shams R, Sadeghi P, Kennedy R et al (2010) Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical images. Comput Methods Programs Biomed 99(2):133–146PubMedCrossRef
25.
Dai Y, Tian J, Dong D et al (2010) Real-time visualized freehand 3D ultrasound reconstruction based on GPU. IEEE Trans Inf Technol Biomed 14(6):1338–1345PubMedCrossRef
26.
Sidiropoulos K, Glotsos D, Kostopoulos S et al (2012) Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit. Comput Biol Med 42(4):376–386PubMedCrossRef
27.
Kirk DB, W-mW Hwu (2010) Programming massively parallel processors: a hands-on approach. Morgan Kaufmann, Amsterdam
28.
BI-RADS Breast Imaging Reporting and Data System Breast Imaging Atlas (2003) American College of Radiology. Reston, VA
29.
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Sys Man Cyb 3:610–621CrossRef
30.
Galloway MM (1975) Texture analysis using gray-level run lengths. Comput Graph Image Process 4:172–179CrossRef
31.
Gose E, Johnsonbaugh R, Jost S (1996) Pattern recognition and image analysis. Prentice Hall PTR, New Jersey
32.
Gonzalez R, Woods R (2002) Digital image processing. 2nd edn. Addison-Wesley Pub, NY
33.
Specht DF (1990) Probabilistic neural networks. Neural Netw 3:109–118CrossRef
34.
Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, Hoboken, NJCrossRef
35.
Kecman V (2001) Learning and soft computing: Support vector machines, Neural Networks, and Fuzzy logic models. MIT Press, Cambridge, pp 121–184
36.
Gunal S, Gerek ON, Ece DG et al (2009) The search for optimal feature set in power quality event classification. Expert Syst Appl 36(7):10266–10273CrossRef
37.
Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci USA 99(10):6562–6566PubMedCrossRef
38.
Romero E, Sopena JM (2008) Performing feature selection with multilayer perceptrons. IEEE Trans Neural Netw 19(3):431–441PubMedCrossRef
39.
Ahmed N, Rao KR (1975) Orthogonal transforms for digital signal processing. Springer, BerlinCrossRef
40.
Oh K-S, Jung K (2004) GPU implementation of neural networks. Pattern Recognit 37(6):1311–1314CrossRef
41.
Beliakov G, Li G (2012) Improving the speed and stability of the k-nearest neighbors method. Pattern Recognit Lett 33(10):1296–1301CrossRef
42.
Pang WM, Qin J, Lu Y et al (2011) Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU. Int J Comput Assist Radiol Surg 6(2):187–199PubMedCrossRef
43.
Santhanam AP, Min Y, Neelakkantan H et al (2012) A multi-GPU real-time dose simulation software framework for lung radiotherapy. Int J Comput Assist Radiol Surg 7(5):705–719PubMedCrossRef
44.
Foley D (1972) Considerations of sample and feature size. IEEE Trans Inf Theory 18(5):618–626CrossRef
45.
Karahaliou AN, Boniatis IS, Skiadopoulos SG et al (2008) Breast cancer diagnosis: analyzing texture of tissue surrounding microcalcifications. IEEE Trans Inf Technol Biomed 12(6):731–738PubMedCrossRef
Metadaten
Titel
Multimodality GPU-based computer-assisted diagnosis of breast cancer using ultrasound and digital mammography images
verfasst von
Konstantinos P. Sidiropoulos
Spiros A. Kostopoulos
Dimitris T. Glotsos
Emmanouil I. Athanasiadis
Nikos D. Dimitropoulos
John T. Stonham
Dionisis A. Cavouras
Publikationsdatum
01.07.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 4/2013
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-013-0813-y

Weitere Artikel der Ausgabe 4/2013

International Journal of Computer Assisted Radiology and Surgery 4/2013 Zur Ausgabe

Original Article

A robust method to track colonoscopy videos with non-informative images

Original Article

An evaluation of image descriptors combined with clinical data for breast cancer diagnosis

Original Article

Discriminative generalized Hough transform for object localization in medical images

Original Article

Three-dimensional histological specimen preparation for accurate imaging and spatial reconstruction of the middle and inner ear

Original Article

Comparative exploration of whole-body MR through locally rigid transforms

Original Article

Mobile augmented reality for computer-assisted percutaneous nephrolithotomy