Abstract
The segmentation is the process of extraction of the desired region of interest and it plays a vital role in computer vision and image processing. The conventional watershed segmentation is prone to over segmentation due to the presence of noise. The nonlinear tensor diffusion filtering is used for preprocessing the CT/MR images prior to segmentation and its performance is superior to conventional spatial domain filters like median, Gaussian, and bilateral filter. The preprocessed image was subjected to marker-controlled watershed segmentation; satisfactory results were produced when compared with the morphological gradient and area open morphological gradient watershed approaches. The algorithms were developed in Matlab 2010a and tested on abdomen CT and knee MR images.
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The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015).
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Kumar, S.N., Lenin Fred, A., Ajay Kumar, H., Sebastian Varghese, P. (2018). Nonlinear Tensor Diffusion Filter Based Marker-Controlled Watershed Segmentation for CT/MR Images. In: Chaki, N., Cortesi, A., Devarakonda, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-6319-0_27
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DOI: https://doi.org/10.1007/978-981-10-6319-0_27
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