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An Efficient Approach To Track Rbc And Detect Blood Disease Using Blood Samples


Author(s):

KIRAN TIWARI , Nuva College of Engineering and Technology, Nagpur, Maharashtra, India; Prof. Mrs. Pooja Thakre, Nuva College of Engineering and Technology, Nagpur, Maharashtra, India

Keywords:

Red Blood Cells, Hough Transform; Roughest theory; segmentation; White Blood Cell

Abstract:

The measure of WBC and RBC Cells are very crucial to diagnose various diseases. Diseases like anaemia, leukaemia etc. can easily diagnose by calculation of WBC and RBC. Healthcare industries are focusing on the approach to generate report of blood cell count in fast and cost-effective way. Conventional method of manual measurement of red blood cell under a microscope yields incorrect results, consumes more time and very expensive. In market, there are numerous systems available for the automatic quantification of blood cells. These systems allow counting the number of different types of cells within the blood smear slides. The aim objective of this research is to produce a survey on computer vision system used image processing algorithms to detect and estimate the number of red blood cells in the blood sample image. In this project, image processing algorithms are used for counting of blood cells. Image processing algorithms involve six major steps: image acquisition, pre-processing, image enhancement, image segmentation, feature extraction and counting algorithm. In this project, segmentation, detection, and counting red blood cells in the blood sample image is carried out using Hough Transform, Roughest theory and KNN method.


Other Details:

Manuscript Id :IJSTEV2I10128
Published in :Volume : 2, Issue : 10
Publication Date: 01/05/2016
Page(s): 542-546
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