..............................
..............................
..............................
A Novel Approach for Segmentation of Human Metaphase Chromosome Images Using Region
The chromosomes are the genetic information carries. A healthy human being has 46 chromosomes. Any alteration
in either the number of chromosomes or the structure of chromosomes in a human being is diagnosed as a genetic defect. To
uncover the genetic defects the metaphase chromosomes are imaged and analyzed. The metaphase chromosome images often
contain intensity inhomogeneity that makes the image segmentation task difficult. The difficulties caused by intensity
inhomogeneity can be resolved by using region based active contours techniques. These techniques uses the local intensity
values of the nearby regions of the objects and find the approximate intensity values along both sides of the contour. In the
proposed work a segmentation technique has been proposed to segment the objects present in the human metaphase
chromosome images using region based active contours. The proposed technique has been quite efficient from prospective of
number of objects segmented. The method has been tested on Advanced Digital Imaging Research (ADIR) dataset. The
experimental results have shown quite good performance.
[1] Agam G. and Dinstein I., “Geometric Separation of Partially Overlapping Nonrigid Objects Applied to Automatic Chromosome Classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1212-1222, 1997.
[2] Arora T. Dhir R., “Segmentation of Human Metaspread Images Using Region Based Active Contours,” in Proceedings of in International Conference on Recent Trends in Engineering and Material Science, Jaipur, pp. 1-5, 2016.
[3] Arora T. and Dhir R., “An Efficient Segmentation Method for Overlapping Chromosome Images,” International Journal of Computer Applications, vol. 95, no. 1, pp. 29-32, 2014.
[4] Arora T. and Dhir R., “A Review of Metaphase Mhromosome Image Selection Techniques for Automatic Karyotype Generation,” Medical and Biological Engineering and Computing, vol. 54, no. 8, pp. 1147-1157, 2015.
[5] Arora T. and Dhir R., “Segmentation Approaches for Human Metaspread Chromosome Images Using Level Set Methods,” in Proceedings of International Conference on Mass Data Analysis of Images and Signals, New York, pp. 13-30, 2017.
[6] Bickmore W., Karyotype Analysis and Chromosome Banding, Wiley Online Library, 2001.
[7] Castleman K., Choi H., and Bovik A., “Maximum-likelihood Decomposition of Overlapping and Touching M-FISH Chromosomes using Geometry, Size and Color Information,” in Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Society, New York, pp. 3130-3133, 2006.
[8] Granlund G., “Identification of Human Chromosomes by Using Integrated Density Profiles,” IEEE Transactions on Biomedical Engineering, vol. 23, no. 3, pp. 182-192, 1976.
[9] Grisan E., Poletti E., and Ruggeri A., “An Improved Segmentation of Chromosomes in Q- Band Prometaphase Images Using a Region Based Level Set,” in Proceedings of World Congress on Medical Physics and Biomedical Engineering, Munich, pp. 748-751, 2009.
[10] Ji L., “Intelligent Splitting in the Chromosome Domain,” Pattern Recognition, vol. 22, no. 5, pp. 519-532, 1989.
[11] Ji L., “Fully Automatic Chromosome Segmentation,” Cytometry, vol. 17, no. 3, pp. 196-208, 1994.
[12] Karvelis P., Likas A., and Fotiadis D., “Identifying Touching and Overlapping Chromosomes Using the Watershed Transform and Gradient Paths,” Pattern Recognition Letters, vol. 31, no. 16, pp. 2474-2488, 2010.
[13] Kumar T. and Reddy K., “A Technique for Burning Area Identification Using IHS Transformation and Image Segmentation,” The International Arab Journal of Information Technology, vol. 12, no. 6A, pp. 764-771, 2015.
[14] Li C., Huang R., Ding Z., Gatenby J., Metaxas D., and Gore J., “A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with Application to MRI,” IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 2007-2016, 2011.
[15] Li C., Kao C., Gore J., and Ding Z., “Minimization of Region-Scalable Fitting Energy for Image Segmentation,” IEEE Transactions on Image Processing, vol. 17, no. 10, pp. 1940- 1949, 2008.
[16] Minaee S., Fotouhi M., and Khalaj B., “A Geometric Approach to Fully Automatic Chromosome Segmentation,” in Proceedings of IEEE Signal Processing in Medicine and Biology Symposium, Philadelphia, pp. 1-6, 2011.
[17] Moallem P., Karimizadeh A., and Yazdchi M., “Using Shape Information and Dark Paths for Automatic Recognition of Touching and Overlapping Chromosomes in G-Band Images,” International Journal Image, Graphics and Signal Processing, vol. 5, no. 4, pp. 22-28, 2013.
[18] Nickolls P., Piper J., Rutovitz D., Chisholm A., Joitnstone I., and Robertson M., “Pre-Processing of Images in an Automated Chromosome Analysis System,” Pattern Recognition, vol. 14, no. 1-6, pp. 219-229, 1981.
[19] Oosterlinck A., Daele J., Boer J., Dom F., Reynaerts A., Berghe H., “Computer-Assisted Karyotyping with Interaction,” The Journal of Histochemistry and Cytochemistry, vol. 25, no. 7, pp. 754-762, 1977.
[20] Piper J. and Granum E., “On Fully Automatic Feature Measurement for Banded Chromosome A Novel Approach for Segmentation of Human Metaphase Chromosome Images ... 137 Classification,” Cytometry, vol. 10, no. 3, pp. 242-255, 1989.
[21] Poletti E., Zappelli F., Ruggeri A., and Grisan E., “A Review of Thresholding Strategies Applied to Human Chromosome Segmentation,” Computer Methods and Programs in Biomedicine, vol. 108, no. 2, pp. 679-688, 2012.
[22] Schwartzkopf W., Bovik A., and Evans B., “Maximum-Likelihood Techniques for Joint Segmentation-Classification of Multispectral Chromosome Images,” IEEE Transactions on Medical Imaging, vol. 24, no. 12, pp. 1593-1610, 2005.
[23] Shemilt L., Verbanis E., Schwenke J., Estandarte A., Xiong G., Harder R., Parmar N., Yusuf M., Zhang F., and Robinson K., “Karyotyping Human Chromosomes by Optical and X-Ray Ptychography Methods,” Biophysj, vol. 108, no. 3, pp. 706-713, 2015.
[24] Somasundaram D. and Kumar V., “Separation of Overlapped Chromosomes and Pairing of Similar Chromosomes for Karyotyping Analysis,” Measurement, vol. 48, pp. 274-281, 2014.
[25] Srisang W., Jaroensutasinee K., and Jaroensutasinee M., “Segmentation of Overlapping Chromosome Images Using Computational Geometry,” Walailak Journal of Science and Technology, vol. 3, no. 2, pp. 181- 194, 2006.
[26] Tjio J. and Levan A., “The Chromosome Number of Man,” Genetics, vol. 42, no. 1-2, pp. 1-6, 1956. Tanvi Arora is currently working as associate professor in the department of Computer Science & Engineering at Baddi University of Emerging Sciences and Technology, Baddi, Himachal Pradesh, India. Her teaching and research interests include Image Processing, Pattern Recognition.