Color homogram for segmentation of fine needle biopsy images

• Autorzy: Marciniak A. , Nieczkowski T. , Obuchowicz A.
• Języki publikacji: EN

Abstrakty

EN

In this paper, a new weighted clustering algorithm for image segmentation in cytopathology is introduced. The weights incorporating spatial information into pixel-based segmentation are computed with use of a color homogram. The effectiveness of the proposed solution is evaluated on microscopic fine needle biopsy (FNB) images. The results of the classical fuzzy c-means algorithm and its weighted modification are compared.

Słowa kluczowe

EN

homogeneity; color image segmentation; clustering; fine needle biopsy; fuzzy logic

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2008
• Tom: Vol. 17, No. 1/2
• Strony: 153--165
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Marciniak A.

autor

Nieczkowski T.

autor

Obuchowicz A.

• Institute of Control and Computation Engineering, 50 Podgorna str., University of Zielona Gora, Poland

Bibliografia

• [1] Ohlander R., Price K., Reddy D. R.: Picture segmentation using a recursive region splitting method. Computer Graphics and Image Processing, 8, 313-333, 1978.
• [2] Ohta Y. L, Kanade T., Sakai T.: Color information for region segmentation. Computer Graphics and Image Processing, 13, 222-241, 1980.
• [3] Fu K. S., Mui J. K.: A survey on image segmentation. Pattern Recognition, 13, 3-16, 1981.
• [4] Wyszecki G., Stiles W. S.: Color science. Wiley, New York, 1982.
• [5] Haralick R. M. , Shapiro L. G.: Image segmentation techniques. Computer Vision Graphics Image Processing, 29, 100-132, 1985.
• [6] Sahoo P. K., Soltani S., Wong A. K. C., Chen Y. C.: A survey of thresholding techniques. Computer Vision Graphics Image Processing, 41, 233-260, 1988.
• [7] Pratt W. K.: Digital image processing.Wiley, New York, 1991.
• [8] Skarbek W., Koschan A.: Colour image segmentation -A Survey. Technischer Bericht 94-32, TU
• [9] Brink A. D.: Using spatial information as an aid to maximum entropy image threshold selection. Pattern Recognition, 17, 29-36, 1996.
• [10] Jawahar C. V. , Biswas P. K., Ray A. K.: Investigations on fuzzy thresholding based on fuzzy clustering. Pattern Recognition, 30, 1605-1613, 1997.
• [11] Leung C. K., Lam F. K.: Maximum a posteriori spatial probability segmentation. IEE Proc. Vis. Image Signal Processing, 144 (3), 181-167, 1997.
• [12] Cheng H. D., Chen C. H., Chiu H. H., Xu H.: Fuzzy homogeneity approach to multilevel thresholding. IEEE Trans. Image Processing, 7 (7), 1084-1088, 1998.
• [13] Androutsos D.: Efficient Indexing and retrieval of color image data using a vector-based approach. Ph.D. dissertation, Toronto Univ., 1999.
• [14] Lucchese L., Mitra S.: Advances in color image segmentation. Proc. of Globecom'99, 4, 2038-2044, Rio de Janeiro, Brazil, 1999.
• [15] Ikonomakis N., Plataniotis K., Venetsanopoulos A.: Color image segmentation for multimedia applications, Journal of Intelligent and Robotic Systems, 28 (1-2), 5-20, 2000.
• [16] Zhang B.: Generalized k-harmonic means - boosting in unsupervised learning. Technical Report HPL-137, Hewlett Packard Software Technology Laboratory.
• [17] Duda R. O., Hart P. E., Stork D. G.: Pattern Classification. Wiley, New York, 2nd edition, 2000.
• [18] Cheng H. D., Jiang X. H., Sun Y., Wang J.: Color image segmentation: advances and prospects, Pattern Recognition, 34( 12), 2259-2281, 2001.
• [19] Leski J.: An insensitive approach to fuzzy clustering. Int. J. Applied Mathematics&Computer Science, 11 (4), 993-1007, 2001.
• [20] Cheng H. D., Jiang X.H., Wang J.: Color image segmentation based on homogram thresholding and region merging. Pattern Recognition, 35, 373-393, 2002.
• [21] Gonzalez R. C., Woods R. E.: Digital Image Processing. Prentice Hali, New Jersey, 2nd edition, 2002.
• [22] Hammerly G., Elkan C.: Alternatives to the k-means algorithm that find better clusterings. Proc. of the 11th ACM Int. Conf. on Information and Knowledge Management, Press New York, 600-607, 2002.
• [23] Odet C., Zouagui T., Benoit-Cattin H.: Image segmentation functional model. Pattern Recognition, 37 (9), 1785-1795, 2002.
• [24] Yan F., Zhang H., Kube C. R.: A multistage adaptive thresholding method. Patera Recognition, 26, 1183-1191, 2004.
• [25] Kwon S. H.: Threshold selection based on cluster analysis. Pattern Recognition, 25, 1045-1050, 2004.
• [26] Marciniak A., Obuchowicz A., Monczak R., Kolodzinski M.: Cytomorphometry of fine needle biopsy materiał from the breast cancer. Advances in Soft Computing. Proc. of the 4th Int. Conf. on Comp. Recognition Systems CORES' 05, 603-609, 2005.
• [27] Xu R., Wunsch D.: Survey of clustering algorithms. IEEE Trans. Neural Networks, 16 (3), 645-678, 2005.