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
Erschienen in:
The Analysis of Medical Images Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

6. Segmentation: Principles and Basic Techniques

verfasst von : Klaus D. Toennies

Erschienen in: Guide to Medical Image Analysis

Verlag: Springer London

Einloggen

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

search-config
loading …

Abstract

The purpose of image segmentation is to generate pixel agglomerations from an image that constitute parts of depicted objects. In medical imaging, segmentation often refers to the delineation of specific structures. Hence, it includes parts of classification as well. Segmentation strategies in medical imaging combine data knowledge with domain knowledge to arrive at the result. Data knowledge refers to assumptions about continuity, homogeneity, and local smoothness of image features within segments. Domain knowledge represents information about the objects to be delineated. In this chapter, basic strategies for integrating the two types of knowledge into the segmentation process will be discussed. We will also describe basic segmentation methods that are popular in medical image analysis.

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 "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

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
Vorheriges Kapitel Feature Detection
Nächstes Kapitel Segmentation in Feature Space
Fußnoten
1
If the model is mainly based on its parameterization and requires frequent feedback, it again would indicate a poor design of the segmentation model. This should not happen in practice, because in such case the success of segmentation would vary substantially with different parameter settings making segmentation time-consuming and awkward to use.
 
2
Thresholding is essentially a classification in feature space as it finds a decision boundary. If the feature space is multi-dimensional, the user should resort to classification techniques that are described in Chaps. 7 and 12.
 
Literatur
Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6):641–647CrossRef
Barrett WA, Mortensen EN (1997) Interactive live-wire boundary extraction. Med Image Anal 1(4):331–341CrossRef
Beucher S (1994) Watershed, hierarchical segmentation and waterfall algorithm. In: Serra J, Soille P (eds) Mathematical morphology and its application to image and signal processing. Kluwer Academic Publishers, pp 69–76
Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan NM, Tao D, Cheng CY, Aung T, Wong TY (2013) Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imag 32(6):1019–1032CrossRef
Cheriet M, Said JN, Suen CY (1998) A recursive thresholding technique for image segmentation. IEEE Trans Image Process 7(6):918–921CrossRef
Falcão AX, Udupa JK (2000) A 3d generalization of user-steered live-wire segmentation. Med Image Anal 4(4):389–402CrossRef
Falcão AX, Udupa JK, Samarasekera S, Sharma S, Hirsch BE, de Lotufo AR (1998) User-steered image segmentation paradigms: live wire and live lane. Graph Models Image Process 60(4):233–260CrossRef
Grau V, Mewes AUJ, Alcañiz M, Kikinis R, Warfield SK (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23(4):447–458CrossRef
Haralick RM, Shanmugam K, Dinstein KI (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet 3(6):610–621CrossRef
Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK (1998) Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 7(12):1684–1699CrossRef
Hou Z (2006) A review on MR image intensity inhomogeneity correction. Int J Biomed Imag: 1–11
Lorigo LM, Faugeras O, Grimson WEL, Keriven R, Kikinis R (1998) Segmentation of bone in clinical knee MRI using texture-based geodesic active contours. In: 1st international conference medical image computing and computer-assisted intervention—MICCAI 1998, LNCS vol 1496, pp 1195–1204
Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc Lond B 207(1167):187–217
Meyer F, Beucher S (1990) Morphological segmentation. J Vis Commun Image Represent 1(1):21–46CrossRef
Mortensen EN, Morse B, Barrett W, Udupa JK (1992) Adaptive boundary detection using ‘live-wire’ two-dimensional dynamic programming. Proc Comput Cardiol, 635–638
Mortensen EN, Barrett WA (1995) Intelligent scissors for image composition. In: Proceedings of the 22nd International Conference, SIGGRAPH 95, pp 191–198
Muzzolini R, Yang YH, Pierson R (1993) Multiresolution texture segmentation with application to diagnostic ultrasound images. IEEE Trans Med Imaging 12(1):108–123CrossRef
Najman L, Schmitt M (1996) Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans Pattern Anal Mach Intell 18(12):1163–1173CrossRef
Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intell 26(11):1452–1458CrossRef
Otsu N (1978) A threshold selection method from grey-level histograms. IEEE Trans Syst Man Cybernet SMC-8:62–66
Pitiot A, Toga AW, Ayache N, Thompson P (2002) Texture based MRI segmentation with a two-stage hybrid neural classifier. In: Proceedings of the international joint conference neural networks (IJCNN’02), vol 3, pp 2053–2058
Pohle R, Toennies KD (2001) Segmentation of medical images using adaptive region growing. Proc SPIE (Med Imaging 2001) 4322:1337–1346CrossRefMATH
Reyes-Aldasoro CC, Bhalerao A (2003) Volumetric texture description and discriminant feature selection for MRI. In: Information processing in medical imaging, IPMI 2003, LNCS, vol 2732, pp 282–293
Roerdink JBTM, Meijster A (2000) The watershed transform: definitions, algorithms and parallelization strategies. Fundam Inf 41:187–228MathSciNetMATH
Rosenfeld A, Smith RC (1981) Thresholding using relaxation. IEEE Trans Pattern Anal Mach Intell 3(5):598–606CrossRef
Sahoo PK, Soltani S, Wong AKC, Chen YC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41(2):233–260CrossRef
Schenk A, Prause GPM, Peitgen HO (2001) Local-cost computation for efficient segmentation of 3D objects with live wire. Proc SPIE (Med Imaging 2001) 4322:1357–1364CrossRef
Tobias OJ, Seara R (2002) Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process 11(12):1457–1465CrossRef
Tomazevic D, Likar B, Pernus F (2002) Comparative evaluation of retrospective shading correction methods. J Microsc 208(Pt 3):212–223MathSciNetCrossRefMATH
Vincent JL (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2:176–201CrossRef
Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3):405–421CrossRef
Wan SY, Higgins WE (2003) Symmetric region growing. IEEE Trans Image Process 12(9):1007–1015CrossRef
Xu X, Niemeijer M, Song Q, Sonka M, Garvin MK, Reinhardt JM, Abramoff MD (2011) Vessel boundary delineation on fundus images using graph-based approach. IEEE Trans Med Imaging 30(6):1184–1191CrossRef
Zack G, Rogers W, Latt S (1977) Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 25:741–753CrossRef
Zucker SW (1976) Region growing: childhood and adolescence. Comput Graph Image Process 5(3):382–399CrossRef
Metadaten
Titel
Segmentation: Principles and Basic Techniques
verfasst von
Klaus D. Toennies
Copyright-Jahr
2017
Verlag
Springer London
Buch
Guide to Medical Image Analysis

Print ISBN: 978-1-4471-7318-2

Electronic ISBN: 978-1-4471-7320-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-1-4471-7320-5_6