Abstract
Image segmentation is necessary but significant element in less intensity image investigation, pattern recognition, and in robotic systems. It is one of the most complex and demanding tasks in image processing. Image segmentation is the process of separating an image into various regions such that each region is identical. This paper proposes a new medical image segmentation method that integrates multi-resolution wavelet packet decomposition with the watershed transform for MRI image. The wavelet packet transform (WPT) is applied to the input image, creating detail and approximation coefficients. If watershed technique alone is used for segmentation, then over cluster is present. To overcome this, the proposed technique which combines wavelet packet and watershed algorithm is developed. First, the wavelet packet transform is applied to produce multi-resolution images, followed by applying watershed for segmentation to the approximation sub-bands. Finally, Inverse WPT is implemented to obtain the segmented image. Due to wavelet packet decomposition, the quantity of the disturbance can be decreased and leads to a tough segmentation. This proposed work concludes that wavelet packet and watershed transform facilitate to get the elevated precision even in strident images.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Patt. Recognit. 26(9), 1277–1294 (1993)
Kim, H.J., Kim, E.Y., Kim, J.W., Park, S.H.: MRF model based image segmentation using hierarchical distributed, genetic algorithm. IEE Electron. Lett. 34(25), 1394–1395 (1998)
Kim, J.B., Kim, H.J.: Multi-resolution based watersheds for efficient image segmentation. Patt. Recogn. Lett. 24, 473–488 (2003)
Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. In: Mathematical Morphology in Image Processing. Marcel Dekker, New York (1993)
Haris, K., Efstratiadis, S.N., Maglaveras, N., Katsaggelos, A.K.: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7(12), 1684–1699 (1998)
Gies, V., Bernard, T.: Statistical solution to watershed over-segmentation. In: International Conference on Image Processing, pp. 1863–1866 (2004)
Ma, W.Y., Manjunath, B.S.: Edge flow: a technique for boundary detection and image segmentation. IEEE Trans. Image Process. 9(8), 1375–1388 (2000)
Mallat, G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
RajMohan, K., Thirugnanam, G., Mangaiyarkarasi, P. (2017). Hybrid Segmentation Technique Using Wavelet Packet and Watershed Transform for Medical Images. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_14
Download citation
DOI: https://doi.org/10.1007/978-981-10-5427-3_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5426-6
Online ISBN: 978-981-10-5427-3
eBook Packages: Computer ScienceComputer Science (R0)