A hybrid approach for oestrogen receptor status evaluation

REFERENCES

• Zhou H, Mao KZ. Adaptive successive erosion-based cell image segmentation for p53 immunohistochemistry in bladder inverted papilloma, Proc. 27th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society: IEEE-EMBS, Shanghai, China, September 2005, Engineering in Medicine and Biology Society, pp. 6484–6487.
   Google Scholar
• Di Ruberto C, Dempster A, Khan S, Jarra. B. Analysis of infected blood cell images using morphological operators. Image Vis. Comput., 2002, 20, 133–146.
   Web of Science ®Google Scholar
• You Y, Yu H. A separating algorithm based on granulometry for overlapping circular cell images, Proc. 2004 Int. Conf. on Intelligent mechatronics and automation: ICIMA 2004, Chengdu, China, August 2004, IEEE Robotics and Automation Society, pp. 244–248.
   Google Scholar
• Jain AK, Murthy MN, Flynn PJ. Data clustering: a review. ACM Comput. Rev., 1999, 31, 264–323.
   Web of Science ®Google Scholar
• Kotsiantis SB, Pintelas PE. Recent advances in clustering: a brief survey. WSEAS Trans. Inf. Sci. Appl., 2004, 1, 73–81.
   Google Scholar
• Xu R, Wunsch D. Survey of clustering algorithms. IEEE Trans. Neural Networks, 2005, 16, 645–678.
   PubMed Web of Science ®Google Scholar
• Zadeh LA. Fuzzy sets. Inf. Control, 1965, 8, 338–353.
   Web of Science ®Google Scholar
• Ruspini EH. A new approach to clustering. Inf. Control, 1969, 15, 22–32.
   Google Scholar
• Dunn JC. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J. Cybern., 1973, 3, 32–57.
   Google Scholar
• Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms, 1981 (Plenum Press, New York).
   Google Scholar
• Hung WL, Yang MS, Chen DH. Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Patt. Recogn. Lett., 2006, 27, 424–438.
   Web of Science ®Google Scholar
• Zhang DQ, Chen SC. A novel kernelized fuzzy C-means algorithm with application in medical image segmentation. Artif. Intell. Med., 2004, 32, 37–50.
   PubMed Web of Science ®Google Scholar
• Zhang GP. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev., 2000, 30, 451–462.
   Web of Science ®Google Scholar
• Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm, Proc. IEEE Int. Conf. on Neural networks: ICNN ’93, San Francisco, CA, USA, March–April 1993, IEEE Computer Society, pp. 586–591.
   Google Scholar
• Hagan MT, Demuth HB, Beale MH. Neural Network Design, 1996 (PWS Publishing, Boston, MA).
   Google Scholar
• Matheron G. Random Sets and Integral Equation, 1978 (Wiley, New York).
   Google Scholar
• Mohana KNR, Dempster AG. Area-granulometry: an improved estimator of size distribution of image objects. IEE Electron. Lett., 2001, 37, 950–951.
   Web of Science ®Google Scholar
• Meyer F. Contrast features extraction. In Quantitative Analysis of Microstructures in Material Sciences, Biology and Medicine (Ed. , Chermant J L, ed), 1977, (Rieder Verlag, Stuttgart) pp. 374–380.
   Google Scholar
• Soille P. Morphological Image Analysis: Principles and Applications, 1999, (Springer-Verlag, Berlin).
   Google Scholar
• Beucher S, Lantuéjoul C. Use of watershed in contour detection, Proc. Workshop on Image processing, real-time edge and motion detection/estimation, Rennes, France, September, , 1979, .
   Google Scholar
• Breu H, Gil J, Kirkpatrick D, Werman M. Linear time Euclidean distance transform algorithms. IEEE Trans. Patt. Anal. Mach. Intell., 1995, 17, 529–533.
   Web of Science ®Google Scholar
• Sternberg RJ. Criteria for intellectual skills training. Educ. Res., 1983, 12, 6–12.
   Google Scholar
• Digabel H, Lantuejoul C. In Quantitative Analysis of Microstructures in Material Science, Biology and Medicine, (Ed. , Chermant J L, ed), 1978 (Riederer-Verlag, Stuttgart),
   Google Scholar
• Roerdink J, Meijster A. The watershed transform: definition, algorithms and parallelization strategies. Fundam. Inf., 2001, 41, 187–228.
   Google Scholar
• Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell., 1991, 13, 583–598.
   Web of Science ®Google Scholar
• Meyer F. Sequential algorithms for cell segmentation: maximum efficiency, Proc. Int. Symp. on Clinical cytometry and histomery, Schloss Elmau, Germany, April, , 1986.
   Google Scholar