Analysis of Blood Smear Images for Leukaemia Detection Using Data Mining Algorithms

Acharya, Vasundara and Kumar, Preetham (2016) Analysis of Blood Smear Images for Leukaemia Detection Using Data Mining Algorithms. International Journal of Engineering Research in Electronics and Communication, 3 (11). pp. 18-24. ISSN 2394-6849

[img] PDF
2792.pdf - Published Version
Restricted to Registered users only

Download (249kB) | Request a copy

Abstract

Blood cell segmentation and extraction of characteristics of cells plays a vital role in the field of medicine. Blood count is used to analyze the overall health of a person. It can be used to identify diseases like anemia, infection and leukemia. White Blood cells, Red Blood cells and platelets forms the parts of blood. In laboratory, blood cell counting is done by using counting chamber known as Hemocytometer, Petri dish and microscope. The entire procedure involves the use of physician’s skills to prepare the plating which is very time consuming and inaccurate task. The aim of this research is to perform a survey on computer aided system that can detect and estimate the number of red blood cells and white blood cells in the blood smear image using image processing algorithms. Image processing algorithms involves the following steps: Inputting the image, pre-processing, image segmentation, feature extraction and applying appropriate counting algorithm. The main objective here is to gain knowledge about the different methodologies used for counting of red blood cells and extracting features of white blood cells. It also throw light on various research directions used

Item Type: Article
Uncontrolled Keywords: Haemocytometer,Hough-Transform,K-MeansClustering,Petridish,Regionprops
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 28 Jun 2017 06:56
Last Modified: 28 Jun 2017 06:56
URI: http://eprints.manipal.edu/id/eprint/149206

Actions (login required)

View Item View Item