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IJSTR >> Volume 1 - Issue 4, May 2012 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



AUTOMATIC BONE MARROW WHITE BLOOD CELL CLASSFICATION USING MORPHOLOGICAL GRANULOMETRIC FEATURE OF NUCLEUS

[Full Text]

 

AUTHOR(S)

Shraddha Shivhare, Rajesh Shrivastava

 

KEYWORDS

Automatic white blood cell classification, granulometric moments, mathematical morphology, pattern spectrum, white blood cell differential counts.

 

ABSTRACT

The differential counting of white blood cell provides invaluable information to doctors for diagnosis and treatment of many diseases. manually counting of white blood cell is a tiresome, time-consuming and susceptible to error procedure due to the tedious nature of this process, an automatic system is preferable. in this automatic process, segmentation and classification of white blood cell are the most important stages. An automatic segmentation technique for microscopic bone marrow white blood cell images is proposed in this paper. The segmentation technique segments each cell image into three regions, i.e., nucleus, cytoplasm, and background. In this paper, we investigate whether information about the nucleus alone is adequate to classify white blood cells. This is important because segmentation of nucleus is much easier than the segmentation of the entire cell, especially in the bone marrow where the white blood cell density is very high. Even though the boundaries between cell classes are not well-defined and there are classification variations among experts, we achieve a promising classification performance using neural networks with fivefold cross validation in which Bayes classifiers and artificial neural networks are applied as classifiers.The classification performances are evaluated by two evaluation measures: traditional and classwise classificationrates. we compare our results with other classifiers and previously proposed nucleus-based features. The results showthat the features using nucleus alone can be utilized to achieve aclassification rate of 77% on the test sets. Moreover, the classification performance is better in the class wise sense when the a priori information is suppressed in both the classifiers.

 

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