A Novel Transformation Watershed Image Segmentation Model in Digital Elevation Maps Processing
الموضوعات :
Aref Safari
1
(Department of Computer Engineering, Islamic Azad University, Rasht Branch, Rasht, Iran)
الکلمات المفتاحية: image processing, Watershed Algorithm, Maps Production, Computer-integrated Manufacturing Systems,
ملخص المقالة :
Computer analysis of image objects starts with finding them-deciding which pixels belong to each object. Digital elevation maps or models (DEMs) are arrays of numbers representing the spatial distribution of terrain elevations. They can be seen as gray-scale images, whereby the value of a pixel represents an elevation rather than a luminance intensity (the brighter the gray-tone level of a pixel, the higher the elevation of the terrain point corresponding to this pixel). Useful applications of DEMs can be found in civil/rural engineering, geographic information systems (GIS), geomorphology, water resources management, photogrammetry, satellite imaging. A watershed is defined as a region of land that assists in draining water (usually rainwater) into a river or a creek. It is an area of high ground through which water flows into the river or creek. Simply defined, the watershed is a transformation in grayscale images. This technique aims to segment the image, typically when two regions-of-interest are close to each other, their edges touch. Thus far, we have discussed segmentation based on three principal concepts.
[1] Suetens, P., Fua, P. and Hanson, A.J. 1992. Computational Strategies for Object Recognition. ACM Comput. Surv. 24:5–62.
[2] Bomans, M., Hohne, K.H., Tiede, U. and Riemer, M. 2009. 3-D segmentation of MR images of the head for 3-D display. IEEE Trans. Med. Imaging. 9:177–183.
[3] McAuliffe, M.J., Lalonde, F.M., McGarry, D., Gandler,W., Csaky, K. and Trus, B.L. 2012. Medical Image Processing, Analysis and Visualization in clinical research. In Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems, CBMS 2012. 381–386.
[4] Hsu, W.Y. 2015. Segmentation-based compression: New frontiers of telemedicine in telecommunication. Telemat. Inf. 32, 475–485.
[5] Natale, F.G.B.D., Desoli, G.S., Giusto, D.D. and Vernazza, G. 2005. Polynomial approximation and vector quantization: a region-based integration. IEEE Trans. Commun. 43:198–206.
[6] Pham, D.L., Xu, C. and Prince, J.L. 2000. Current Methods in Medical Image Segmentation. Annu. Rev. Biomed. Eng. 2:315–337.
[7] Atta-Fosu, T., Guo, W., Jeter, D., Mizutani, C.M., Stopczynski, N. and Sousa-Neves, R. 2016. 3D Clumped Cell Segmentation Using Curvature Based Seeded Watershed. Journal of Imaging. 2(4):31-37.
[8] Safari, A., Hosseini, R. and Mazinani, M. 2017. A Novel Type-2 Anfis Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster, IJE Transactions B: Applications. 30(11):1568-1577.
[9] Safari, A., Barazandeh, D. and Khalegh Pour, S.A. 2020. A Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis. Journal of Advances in Computer Engineering and Technology. 6(1): 19-2
[10] Meyer, F. Minimum Spanning Forests for Morphological Segmentation. 2017. Mathematical Morphology and Its Applications to Image Processing. Springer: Dordrecht, Netherlands. 77-84.
[11] Wang, D. , 1997. Amultiscale gradient algorithmfor image segmentation using watersheds. Pattern Recognition. 30(12):2043–2052.
[12] Gonzalez, R. C. and Woods, R. E. 2020. Digital Image Processing, Publishing House of Electronics Industry, Beijing, China, 3rd edition.
[13] Jalba, A. C., Wilkinson, M. H. F. and Roerdink, J. B. T. M. 2019. Morphological hat-transform scale spaces and their use in pattern classification. Pattern Recognition. 37(5):901–915.
[14] Jalba, A. C., Roerdink, J. B. T. M. and Wilkinson, M. H. F. 2013. Morphological hat-transform scale spaces and their use in texture classification. Proceedings of the IEEE International Conference on Image Processing. Orlando, USA.
