Abstract
Identification of blood disorders is through visual inspection of microscopic blood cell images. From the identification of blood disorders lead to classification of certain diseases related to blood. We propose an automatic segmentation method for segmenting White blood cell images. Firstly, modified possibilistic fuzzy c-means algorithm is proposed to detect the contours in the image. The GLCM features are extracted and features are selected by MRMR. Adaptive boosting and LS Boosting has been utilized to classify blast cells from normal lymphocyte cells. Comparison performance of classification accuracy was carried out. The effectiveness of the classification system is tested with the total of 80 samples collected. The evaluated results demonstrate that our method outperformed the existing systems with an accuracy of 88 %.
Highlights
The assessment of the white blood cells in the bone marrow of patients is very informative in clinical practice
The result of image segmentation is a collection of segments which combine to form the entire image
Three different classification techniques such as Tree Bagger, LS Boosting and ADA boosting were employed for classification[4] [5] [6], in order to classify the lymphocyte (WBC) as healthy and leukemic
Summary
The assessment of the white blood cells in the bone marrow of patients is very informative in clinical practice. Segmentation is the process of partitioning a digital image into multiple segments based on pixels[7]. Image segmentation is the process of partitioning a digital image into multiple segments. In order to avoid the constraint corresponding to the sum of all typicality values of all data to a cluster must be equal to one cause problems for a big data set. It produces memberships and possibilities simultaneously, with the usual point prototypes or cluster centers for each cluster.
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