Chapter 9 – Image Segmentation

Publisher Summary Region segmentation methods partition an image by grouping similar pixels together into identified regions. Thresholding is an essential region-based image segmentation technique that is particularly useful for scenes containing solid objects resting on a contrasting background. It is computationally simple and never fails to define disjoint regions with closed, connected boundaries. The operation is used to distinguish between the objects of interest (also known as the foreground) and the background on which they lay. The selection of the threshold value is crucial to the success of a thresholding operation. Unless the object in the image has very steep sides, any variation in threshold value can significantly affect the boundary position and thus the overall size of the extracted object. This means that subsequent object measurements, particularly the area measurement, are quite sensitive to the threshold value. While no universal methodology for threshold selection works on all kinds of images, a wealth of techniques have been developed to facilitate the determination of threshold values under different circumstances.

[1]  Fei Liu,et al.  Adaptive thresholding based on variational background , 2002 .

[2]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.

[3]  Terry E. Weymouth,et al.  Using Dynamic Programming For Minimizing The Energy Of Active Contours In The Presence Of Hard Constraints , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[4]  Theo Pavlidis,et al.  Filling algorithms for raster graphics , 1979 .

[5]  G. Zack,et al.  Automatic measurement of sister chromatid exchange frequency. , 1977, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[6]  Chris A. Glasbey,et al.  An Analysis of Histogram-Based Thresholding Algorithms , 1993, CVGIP Graph. Model. Image Process..

[7]  Steven W. Zucker,et al.  Region growing: Childhood and adolescence* , 1976 .

[8]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[9]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  Ramakant Nevatia,et al.  Locating Object Boundaries in Textured Environments , 1976, IEEE Transactions on Computers.

[11]  Stanley R. Sternberg,et al.  Biomedical Image Processing , 1983, Computer.

[12]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[13]  Aysin Ertüzün,et al.  A Multivariate Thresholding Technique for Image Denoising Using Multiwavelets , 2005, EURASIP J. Adv. Signal Process..

[14]  J. C. Noordam,et al.  Multivariate image segmentation with cluster size insensitive fuzzy C-means , 2002 .

[15]  Azriel Rosenfeld,et al.  Connectivity in Digital Pictures , 1970, JACM.

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Alberto Ferrer,et al.  Integration of colour and textural information in multivariate image analysis: defect detection and classification issues , 2007 .

[18]  Theodosios Pavlidis,et al.  Integrating Region Growing and Edge Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Larry S. Davis,et al.  A survey of edge detection techniques , 1975 .

[20]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[21]  Catherine Garbay,et al.  Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  J. F. Brenner,et al.  Two graph searching techniques for boundary finding in white blood cell images. , 1978, Computers in biology and medicine.