Abstract
The purpose of image segmentation is to generate pixel agglomerations from an image that constitute parts of the 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.
Concepts, notions and definitions introduced in this chapter
- Data features: intensity and texture
- The role of homogeneity, smoothness, and continuity in segmentation
- Using object localization and appearance as domain knowledge
- The role of interaction
- Basic segmentation techniques: thresholding, region merging techniques, region growing, watershed transform, live wire
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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.
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References
Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6):641–647
Barrett WA, Mortensen EN (1997) Interactive live-wire boundary extraction. Med Image Anal 1(4):331–341
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, Norwell, pp 69–76
Cheriet M, Said JN, Suen CY (1998) A recursive thresholding technique for image segmentation. IEEE Trans Image Process 7(6):918–921
du Buf JMH, Kardan M, Spann M (1990) Texture feature performance for image segmentation. Pattern Recognit 23(3–4):291–309
Falcão AX, Udupa JK, Samarasekera S, Sharma S, Hirsch BE, de A. Lotufo R (1998) User-steered image segmentation paradigms: live wire and live lane. Graph Models Image Process 60(4):233–260
Falcão AX, Udupa JK (2000) A 3D generalization of user-steered live-wire segmentation. Med Image Anal 4(4):389–402
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):447–458
Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features based on Gabor filters. IEEE Trans Image Process 11(10):1160–1167
Haralick RM, Shanmugam K, Dinstein KI (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621
Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK (1998) Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 7(12):1684–1699
He DC, Wang L (1991) Texture features based on texture spectrum. Pattern Recognit 24(5):391–399
Herman GT, Carvalho BM (2001) Multiseeded segmentation using fuzzy connectedness. IEEE Trans Pattern Anal Mach Intell 23(5):460–474
Hou Z (2006) A review on MR image intensity inhomogeneity correction. Int J Biomed Imag 1–11. doi:10.1155/IJBI/2006/49515.
Julesz B (1975) Experiments in the visual perception of texture. Sci Am 232(4):34–43
Julesz B (1981) Textons, the elements of texture perception, and their interactions. Nature 290:91–97
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 intl conf 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 29, 207(1167):187–217
Meyer F, Beucher S (1990) Morphological segmentation. J Vis Commun Image Represent 1(1):21–46
Mortensen EN, Morse B, Barrett W, Udupa JK (1992) Adaptive boundary detection using ‘live-wire’ two-dimensional dynamic programming. In: Proc computers in cardiology, pp 635–638
Mortensen EN, Barrett WA (1995) Intelligent scissors for image composition. In: SIGGRAPH 95 (Proc 22nd intl conf), 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):108–123
Najman L, Schmitt M (1996) Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans Pattern Anal Mach Intell 18(12):1163–1173
Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intell 26(11):1452–1458
Otsu N (1978) A threshold selection method from grey-level histograms. IEEE Trans Syst Man Cybern SMC-8:62–66
Pitiot A, Toga AW, Ayache N, Thompson P (2002) Texture based MRI segmentation with a two-stage hybrid neuralclassifier. In: Proc intl joint conf neural networks (IJCNN’02), vol 3, pp 2053–2058
Pohle R, Toennies KD (2001) Segmentation of medical images using adaptive region growing. Proc SPIE 4322:1337–1346 (Medical imaging 2001)
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 Inform 41:187–228
Rosenfeld A, Smith RC (1981) Thresholding using relaxation. IEEE Trans Pattern Anal Mach Intell 3(5):598–606
Sahoo PK, Soltani S, Wong AKC, Chen YC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41(2):233–260
Schenk A, Prause GPM, Peitgen HO (2001) Local-cost computation for efficient segmentation of 3D objects with live wire. Proc SPIE 4322:1357–1364 (Medical imaging 2001)
Tobias OJ, Seara R (2002) Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans Image Process 11(12):1457–1465
Tomazevic D, Likar B, Pernus F (2002) Comparative evaluation of retrospective shading correction methods. J Microsc 208(3):212–223
Vincent JL (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2:176–201
Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3):405–421
Wagner T (1999) Texture analysis. In: Jähne B, Haussecker H, Geisser P (eds) Handbook of computer vision and applications. Academic Press, San Diego, pp 275–308
Wan SY, Higgins WE (2003) Symmetric region growing. IEEE Trans Image Process 12(9):1007–1015
Zack G, Rogers W, Latt S (1977) Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 25:741–753
Zucker SW (1976) Region growing: childhood and adolescence. Comput Graph Image Process 5(3):382–399
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Toennies, K.D. (2012). Segmentation: Principles and Basic Techniques. In: Guide to Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2751-2_6
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