Abstract
Here we propose a method for contour detection of cells on medical images. The problem that arises in such images is that cells’ color is very similar to the background, because the cytoplasm is translucent and sometimes overlapped with other cells, making it difficult to properly segment the cells. To cope with these drawbacks, given a cell center, we use hue and saturation histograms for defining the fuzzy sets associated with cells relevant colors, and compute the membership degree of the pixels around the center to these fuzzy sets. Then we approach the color gradient (module and argument) of pixels near the contour points, and use both the membership degrees and the gradient information to drive the deformation of the region borders towards the contour of the cell, so obtaining the cell region segmentation.
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References
He, L., Peng, Z., Everding, B., et al.: A comparative study of deformable contour methods on medical image segmentation. Image and Vision Computing 26, 141–163 (2008)
Ma, Z., Tavares, J.M.R.S., Jorge, R.N.: Segmentation of Structures in Medical Images: Review and a New Computational Framework. In: Proc. Int. Symp. CMBBE 2008 (2008)
Lucchese, L., Mitra, S.K.: Color image segmentation: A state-of-art survey. Proc. Indian Natural Sciences Academy (INSA-A) 67-A, 207–221 (2001)
Cremers, D., Rousson, M., Deriche, R.: A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape. Int. Journal of Computer Vision 72, 195–215 (2007)
Campadelli, P., Casiraghi, E., Esposito, A.: Liver segmentation from computed tomography scans: A survey and a new algorithm. Artificial Intelligence in Medicine, Computational Intelligence and Machine Learning in Bioinformatics 45, 185–196 (2009)
Chan, T., et al.: Active contours without edges. IEEE Trans. Img. Proc. 10, 266–277 (2001)
Song, B., Chan, T.: A Fast Algorithm for Level Set Based Optimization, Univ. California, Los Angeles, Technical Report CAM 02-68 (2002)
Gibou, F., Fedkiw, R.: A fast hybrid k-means level set algorithm for segmentation. In: Proc. of 4th Annual Hawaii Int. Conf. Statistics and Mathematics, pp. 281–291 (2005)
Krinidis, S., Chatzis, V.: Fuzzy Energy-Based Active Contours Image Processing. IEEE Transactions on Image Processing 18, 2747–2755 (2009)
Velez, J., Sanchez, A., Fernandez, F.: Improved Fuzzy Snakes Applied to Biometric Verification Problems. In: Intelligent Systems Design and Applications, pp. 158–163 (2009)
Wesolkowski, S., Jernigan, M., Dony, R.: Comparison of color image edge detectors in multiple color spaces. In: Int. Conf. on Image Processing, vol. 2, pp. 796–799 (2000)
Smith, A.R.: Color gamut transform pairs. Computer Graphics 12, 12–19 (1978)
Sobrevilla, P., Keller, J., Montseny, E.: White Blood Cell Detection in Bone Marrow. In: Proc. of 18th North American Fuzzy Information Processing Society, pp. 403–407 (1999)
Prados-Suárez, B., Sobrevilla, P., Montseny, E., Romaní, S.: On the reliability of the color gradient vector argument approach. In: Proc. IFSA-EUSFLAT, pp. 1863–1868 (2009)
Lankton, S., Nain, D., Yezzi, A., Tannenbaum, A.: Hybrid geodesic region-based curve evolutions for image segmentations. In: Proc. of SPIE Medical Imaging, vol. 6510 (2007)
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Romaní, S., Prados-Suárez, B., Sobrevilla, P., Montseny, E. (2010). Cytoplasm Contour Approximation Based on Color Fuzzy Sets and Color Gradient. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_66
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DOI: https://doi.org/10.1007/978-3-642-14049-5_66
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