Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images.

Karyotyping is an important process to classify chromosomes into standard classes and the results are routinely used by the clinicians to diagnose cancers and genetic diseases. However, visual karyotyping using microscopic images is time-consuming and tedious, which reduces the diagnostic efficiency and accuracy. Although many efforts have been made to develop computerized schemes for automated karyotyping, no schemes can get be performed without substantial human intervention. Instead of developing a method to classify all chromosome classes, we develop an automatic scheme to detect abnormal metaphase cells by identifying a specific class of chromosomes (class 22) and prescreen for suspicious chronic myeloid leukemia (CML). The scheme includes three steps: (1) iteratively segment randomly distributed individual chromosomes, (2) process segmented chromosomes and compute image features to identify the candidates, and (3) apply an adaptive matching template to identify chromosomes of class 22. An image data set of 451 metaphase cells extracted from bone marrow specimens of 30 positive and 30 negative cases for CML is selected to test the scheme's performance. The overall case-based classification accuracy is 93.3% (100% sensitivity and 86.7% specificity). The results demonstrate the feasibility of applying an automated scheme to detect or prescreen the suspicious cancer cases.

[1]  Paul D. Gader,et al.  A fuzzy logic rule-based system for chromosome recognition , 1995, Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems.

[2]  Shibo Li,et al.  Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme , 2009, J. Biomed. Informatics.

[3]  Bin Zheng,et al.  Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives , 2005 .

[4]  P A Errington,et al.  Application of artificial neural networks to chromosome classification. , 1993, Cytometry.

[5]  D. Barba,et al.  “Chromosome”: A rule-based image analysis system for chromosome classification , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  A. Levan,et al.  THE CHROMOSOME NUMBER OF MAN , 2010 .

[7]  Bin Zheng,et al.  Automated Analysis of Fluorescent in situ Hybridization (FISH) Labeled Genetic Biomarkers in Assisting Cervical Cancer Diagnosis , 2010, Technology in cancer research & treatment.

[8]  Jim Graham,et al.  The transportation algorithm as an aid to chromosome classification , 1983, Pattern Recognit. Lett..

[9]  Yuqi Lu,et al.  An expert system for banded chromosomes recognition , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[10]  G. Toussaint.  Solving geometric problems with the rotating calipers , 1983 .

[11]  Henry P. Kramer,et al.  Iterations of a non-linear transformation for enhancement of digital images , 1975, Pattern Recognit..

[12]  J M Cho,et al.  Chromosome classification using backpropagation neural networks. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[13]  Zixiang Xiong,et al.  Subspace-based prototyping and classification of chromosome images , 2005, IEEE Transactions on Image Processing.

[14]  Alan C. Bovik,et al.  Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images , 2005, IEEE Transactions on Medical Imaging.

[15]  Enrico Grisan,et al.  Automatic Segmentation and Disentangling of Chromosomes in Q-Band Prometaphase Images , 2009, IEEE Transactions on Information Technology in Biomedicine.

[16]  E M Manders,et al.  Semi-automated detection of aberrant chromosomes in bivariate flow karyotypes. , 1992, Cytometry.

[17]  J Graham,et al.  A neural network approach to automatic chromosome classification. , 1993, Physics in medicine and biology.

[18]  J. Piper,et al.  On fully automatic feature measurement for banded chromosome classification. , 1989, Cytometry.

[19]  A. Oosterlinck,et al.  On knowledge-based improvement of biomedical pattern recognition-a case study , 1989, [1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications.

[20]  M Van der Ploeg,et al.  DNA measurements of chromosomes 9 and 22 of six patients with t(9;22) and chronic myeloid leukemia. , 1980, Cytometry.

[21]  P. Nowell,et al.  A minute chromosome in human chronic granulocytic leukemia , 1960 .

[22]  Arnold W. M. Smeulders,et al.  Human chromosome classification based on local band descriptors , 1989, Pattern Recognit. Lett..

[23]  K Doi,et al.  Effect of case selection on the performance of computer-aided detection schemes. , 1994, Medical physics.

[24]  S. Delshadpour.  Reduced size multi layer perceptron neural network for human chromosome classification , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[25]  S. McKenna,et al.  Molecular abnormalities in chronic myeloid leukemia: deregulation of cell growth and apoptosis. , 2000, The oncologist.

[26]  M S Beksaç,et al.  An expert diagnostic system based on neural networks and image analysis techniques in the field of automated cytogenetics. , 1996, Technology and health care : official journal of the European Society for Engineering and Medicine.

[27]  W. P. Sweeney,et al.  Classification of chromosomes using a probabilistic neural network. , 1994, Cytometry.

[28]  Ronald L. Graham,et al.  An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set , 1972, Inf. Process. Lett..

[29]  Boaz Lerner,et al.  Toward a completely automatic neural-network-based human chromosome analysis , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[30]  Shibo Li,et al.  A rule-based computer scheme for centromere identification and polarity assignment of metaphase chromosomes , 2008, Comput. Methods Programs Biomed..

[31]  David Gur,et al.  A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. , 2005, Medical physics.

[32]  Qiang Wu,et al.  Fast chromosome karyotyping by auction algorithm , 2005, Int. J. Bioinform. Res. Appl..

[33]  Amala Chaudhuri,et al.  The chromosome number in man , 1963, Indian journal of pediatrics.

[34]  F. Arrighi,et al.  Automated homologue matching of human G-banded chromosomes. , 1986, Computers in biology and medicine.