Automated leukemia detection using hausdorff dimension in blood microscopic images

A cute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. An improved scheme for ALL detection in blood microscopic images is presented here. In this study features i.e. hausdorff dimension and contour signature are employed to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). In addition shape and texture features are also extracted for better classification. Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. Fractal features i.e. hausdorff dimension is implemented for measuring perimeter roughness and hence classifying a lymphocytic cell nucleus. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.

[1]  B. Mandelbrot.  How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[2]  G. Kershaw,et al.  A Beginner’s Guide to Blood Cells , 1997 .

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Dorin Comaniciu,et al.  Cell image segmentation for diagnostic pathology , 2001 .

[5]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[6]  A. G. Ramakrishnan,et al.  Automation of differential blood count , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[7]  Drew Provan.  A Beginner's Guide to Blood Cells, 2nd edn , 2004 .

[8]  Subrajeet Mohapatra,et al.  Development of Impulsive Noise Detection Schemes for Selective Filtering in Images , 2008 .

[9]  J. Suri,et al.  Advanced algorithmic approaches to medical image segmentation: state-of-the-art application in cardiology, neurology, mammography and pathology , 2001 .

[10]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Alex Pentland,et al.  Fractal-Based Description of Natural Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Qingmin Liao,et al.  An accurate segmentation method for white blood cell images , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[13]  M. Palmer,et al.  Routine bone marrow examination in the management of acute lymphoblastic leukaemia of childhood. , 1981, Journal of clinical pathology.

[14]  S. Beksac,et al.  An automated differential blood count system , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  B. Milne.  Measuring the fractal geometry of landscapes , 1988 .

[16]  Banshidhar Majhi,et al.  Improved Enhancement Scheme Using a RBFN Detector for Impulse Noise , 2008, 2008 First International Conference on Emerging Trends in Engineering and Technology.