Abstract
Automatically analyzing morphology of biological objects such as cells, nuclei, and vessels is important for medicine and biology. However, detecting individual biological objects is challenging because biomedical images tend to have a complex structure composed of many morphologically distinct objects and unclear object boundaries. In this paper, we present a novel approach to automatically detect individual objects in biomedical images using a multiple marked point process, in which points are the positions of the objects and marks are their geometric attributes. With this model, we can consider both prior knowledge of the structure of the objects and observed data of an image in object detection. Our proposed method also uses the second generation wavelets-based edge-preserving image smoothing technique to cope with unclear boundaries of biological objects. The experimental results show the effectiveness of our method.
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References
Doi, K.: Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4-5), 198–211 (2007)
Carpenter, A.E., Sabatini, D.M.: Systematic genome-wide screens of gene function. Nature Review Genetics 5, 11–22 (2004)
Yoo, T., Metaxas, D.: Open science – combining open data and open source software: medical image analysis with the Insight Toolkit. Medical Image Analysis 9, 503–506 (2005)
Chang, Y.L., Li, X.: Adaptive image region-growing. IEEE Trans. Image Processing 3(6), 868–872 (1994)
Vincent, L., Soille, P.J.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Machine Intell. 13(6), 583–598 (1991)
Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Machine Intelligence 17(2), 158–175 (1995)
Carpenter, A.E., et al.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology 7, R100 (2006)
Dougherty, E.R.: Mathematical morphology in image processing. CRC press, Boca Raton (1992)
McCullough, D.P., Gudla, P.R., Harris, B.S., Collins, J.A., Meaburn, K.J., Nakaya, M., Yamaguchi, T.P., Misteli, T., Lockett, S.J.: Segmentation of whole cells and cell nuclei from 3D optical microscope images using dynamic programming. IEEE Trans. Medical Imaging 27(5), 723–734 (2008)
Al-Kofahi, Y., Lassoued, W., Lee, W., Roysam, B.: Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomedical Engineering 57(4), 841–852 (2010)
Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comp. Vis. 30(2), 79–116 (1998)
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comp. Vis. 70(2), 109–131 (2006)
Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Medical Imaging 28(9), 1488–1497 (2009)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Intl. J. Comput. Vis. 1(4), 321–331 (1988)
Fattal, R.: Edge-avoiding wavelets and their applications. ACM Trans. Graphics 28(3) (2009)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proc. ICCV, pp. 839–846 (1998)
Sweldens, W.: The lifting scheme: a construction of second generation wavelets. SIAM J. Math. Anal. 29(2), 511–546 (1997)
Gilbert, C.D., Sigman, M.: Brain states: top-down influences in sensory processing. Neuron 54(5), 677–696 (2007)
Descombes, X., Minlos, R., Ehizhina, E.: Object extraction using a stochastic birth-and-death dynamics in continuum. J. Math. Imaging Vis. 33, 347–359 (2009)
Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. IEEE Trans. Pattern Anal. Machine Intelligence 27(10), 1568–1579 (2005)
Ortner, M., Descombes, X., Zerubia, J.: Building outline extraction from digital elevation models using marked point processes. Int. J. Comp. Vis. 72(2), 107–132 (2007)
Green, P.J.: Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995)
Metropolis, M., Rosenbluth, A., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. J. Chemical Physics 21, 1087–1092 (1953)
Abutaleb, A.S.: Automatic thresholding of gray-level pictures using two-dimensional entropy. In: Computer Vision, Graphics, and Image Processing, vol. 47, pp. 22–32 (1989)
Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Modeling and Computer Simulation 8(1), 3–30 (1998)
Gelasca, E.D., Obara, B., Fedorov, D., Kvilekval, K., Manjunath, B.S.: A biosegmentation benchmark for evaluation of bioimage analysis methods. BMC Bioinformatics 10, 368 (2009)
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Hatsuda, H. (2010). Automatic Detection of Morphologically Distinct Objects in Biomedical Images Using Second Generation Wavelets and Multiple Marked Point Process. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_61
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DOI: https://doi.org/10.1007/978-3-642-17277-9_61
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