Cell Nuclei Segmentation In Noisy Images Using Morphological Watersheds

dc.creatorCosta J.A.F.
dc.creatorMascarenhas N.D.A.
dc.creatorDe Andrade Netto M.L.
dc.date1997
dc.date2015-06-30T14:50:10Z
dc.date2015-11-26T15:06:52Z
dc.date2015-06-30T14:50:10Z
dc.date2015-11-26T15:06:52Z
dc.date.accessioned2018-03-28T22:17:16Z
dc.date.available2018-03-28T22:17:16Z
dc.identifier
dc.identifierProceedings Of Spie - The International Society For Optical Engineering. , v. 3164, n. , p. 314 - 324, 1997.
dc.identifier0277786X
dc.identifier10.1117/12.292759
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-0031289877&partnerID=40&md5=aa12b389b831b9f2dd461f777032b038
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/100282
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/100282
dc.identifier2-s2.0-0031289877
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1257324
dc.descriptionA major problem in image processing and analysis is the segmentation of its components. Many computer vision tasks process image regions after segmentation, and the minimization of errors is then crucial for a good automatic inspection system. This paper presents an applied work on automatic segmentation of cell nuclei in digital noisy images. One of the major problems when using morphological watersheds is oversegmentation. By using an efficient homotopy image modification module, we prevent oversegmentation. This module utilizes diverse operations, such as sequential filters, distance transforms, opening by reconstruction, top hat, etc., some in parallel, some in cascade form, leading to a new set of internal and external cell nuclei markers. Very good results have been obtained and the proposed technique should facilitate better analysis of visual perception of cell nuclei for human and computer vision. All steps are presented, as well as the associated images. Implementations were done in the Khoros system using the MMach toolbox.
dc.description3164
dc.description
dc.description314
dc.description324
dc.descriptionCosta, J.A.F., Andrade Netto, M.L., Parts classification in assembly lines using multilayer feedforward neural networks Proc. of the 1997 IEEE International Conference on Systems, Man., and Cybernetics, , Orlando, Florida, October 12-15
dc.descriptionGonzaga, A., Costa, J.A.F., Moment invariants applied to the recognition of objects using neural networks (1996) Proceedings of SPIE, 2847, pp. 223-233. , Applications of Digital Image Processing XIX, Andrew G. Tescher, Editor
dc.descriptionMascarenhas, N.D.A., Velasco, F.R.D., (1989) Processamento digital de imagens. 2a. Ed., , I Escola Brasileiro-Argentina de Informática. Buenos Aires: Ed. Kapelusz
dc.descriptionSilver, D., Object-oriented visualization (1995) IEEE Camputer Graphics and Applications, 15 (3), pp. 54-62. , May
dc.descriptionBallard, D., Brown, C., (1982) Computer Vision, , Prentice-Hall, Englewood Cliffs, N.J
dc.descriptionKass, M., Witkin, A., Terzopoulos, D., Snakes: Active contour models (1988) Int'l. J. Computer Vision, 1 (4), pp. 321-331
dc.descriptionChoi, C., Jennings, A., Learning to segment using fuzzy boundary cell features (1996) Proc. of Complex Systems Conference
dc.descriptionHasegawa, A., Cullen, K.J., Man, S.K., Segmentation and analysis of breast cancer pathological images by na adaptive-sized hybrid neural network (1996) Proc. SPIE, 2710, pp. 752-762. , Medical Imaging: Image Processing, Murray H. Loew
dc.descriptionKenneth M. Hanson
dc.descriptionEds
dc.descriptionBarrera, J., Banon, G.J.F., Lotufo, R.A., Mathematical morphology toolbox for the KHOROS system (1994) Conf. on Image Algebra and Morphological Image Processing V, Intl. Symposium on Optics, Imaging and Instrumentation, , SPIE's Annual Meeting, 24-29 July. San Diego, USA
dc.descriptionMatheron, G., (1975) Random Sets and Integral Geometry, , John Wiley and Sons, New York
dc.descriptionSerra, J., (1982) Image Analysis and Mathematical Morphology, , Academic Press, London
dc.descriptionGiardina, C.R., Dougherty, E.R., (1988) Morphological Methods in Image and Signal Processing, , Prentice-Hall, Englewood Cliffs, NJ
dc.descriptionHaralick, R.M., Sternberg, S.R., Zhuang, X., Image analysis using mathematical morphology (1987) IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, pp. 532-550
dc.descriptionDougherty, E.R., An introduction to morphological image processing (1992) SPIE Tutorial Text, TT09
dc.descriptionHeijmans, H.J.A.M., (1994) Morphological Image Operators, , Academic Press, Boston
dc.descriptionBeucher, S., Lantuéjoul, C., Use of watersheds in contour detection (1979) Proc. Int'l Workshop Image Processing, Real-Time Edge and Motion Detection/Estimation, , Rennes, France, Sept. 17-21
dc.descriptionNajman, L., Schmitt, M., Geodesic saliency of watershed contours and hierarchical segmentation (1996) IEEE Trans. Pattern Anal. Machine Intell., 18 (12), pp. 1163-1173
dc.descriptionVincent, L., Sollie, P., Watersheds in digital space: An efficient algorithm based on immersion simulations (1991) IEEE Trans. on Pattern Anal and Machine Intell., 13 (6), pp. 583-598
dc.descriptionMichael, W.H., William, E.H., Watershed-driven relaxation labeling for image segmentation (1994) Proceedings ICIP-94, 3, pp. 460-463. , IEEE International Conference on Image Processing
dc.descriptionPerry, S., (1996) Fast Interactive Segmentation for Content Based Retrieval and Navigation, , mini-thesis submited for transfer of registration from MPhil to PhD., University of Southampton, UK, October
dc.descriptionMeyer, F., Color image segmentation (1992) 4th International Conference on Image Processing and its Applications, pp. 303-306. , IEE, Conference Publication No. 354
dc.descriptionBeucher, S., Segmentation tools in mathematical morphology (1990) Proceedings SPIE, 1350, pp. 70-84. , Image Algebra and Morphological Image Processing(P.D. Gader, ed.)
dc.descriptionHaris, K., Efstratiadis, S.N., Maglaveras, N., Pappas, C., Hybrid image segmentation using watersheds (1996) Proc. SPIE Vol. 2727, Visual Communications and Image Processing '96, 2727, pp. 1140-1151. , Rashid Ansari
dc.descriptionMark J. Smith
dc.descriptionEds
dc.descriptionMeyer, F., Beucher, S., Morphological segmentation (1990) J. Visual Comm. & Img. Repr., 1, pp. 21-46
dc.descriptionLotufo, R., Trettel, E., Image segmentation by mathematical morphology - Laboratory notes (1996) Brazilian Workshop'96 on Mathematical Morphology, , São Paulo, Feb 27 - March 1
dc.descriptionBarrera, J., Banon, G.J.F., Lotufo, R.A., Hirata R., Jr., (1997) MMach: A Mathematical Morphology Toolbox for the KHOROS System, , Tech. Report RT-MAC-9704. IME/University of São Paulo, São Paulo, Brazil. May
dc.descriptionRasure, J., Jordán, R., Lotufo, R., Teaching image processing with khoros (1994) 1994 IEEE Conf. on Image Processing, , ftp://ftp.dca.fee.unicamp.br
dc.descriptionRasure, J., Williams, C., An integrated data flow visual language and software development environment (1991) Journal of Visual Languages and Computing, pp. 217-246
dc.descriptionKonstatinides, K., Rasure, J., The khoros software development environment for image and signal processing (1994) IEEE Trans. on Image Processing, 3 (3), pp. 243-252. , May
dc.descriptionJordan, R., Lotufo, R., (1994) Digital Image Processing with Khoros 2.0, , World Wide Web (WWW) Courseware
dc.descriptionRasure, J., Kubica, S., Tutorial: The Khoros application development environment (1993) VI Simpósio Brasileiro de Computação Gráfica e Processamento de Imagens, , Recife, PE
dc.languageen
dc.publisher
dc.relationProceedings of SPIE - The International Society for Optical Engineering
dc.rightsaberto
dc.sourceScopus
dc.titleCell Nuclei Segmentation In Noisy Images Using Morphological Watersheds
dc.typeActas de congresos


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