Red Blood Cell Identification Using Watershed Technique
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
Diseases caused by any parasite can be diagnosed using the shape of RBCs and WBCs. The blood sample consists of different cells like RBCs, WBCs, and Platelets from which manual shape analysis may not be accurate. So there is a need in designing an automated system that can identify the shape of RBCs. This paper presents an approach to detect the shape of Sickle cells present in RBCs by finding the highest, lowest and mean radius of each type of cell by comparing it with standard cell size and mark the cells by a red circle for identification. The proposed approach is tested on a collected SEM blood sample images and experimental results are encouraging. The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in the blood sample image. Watershed algorithm is a very powerful tool in image processing, and it is been used to segment and extract the red blood cells from the background and other cells. The algorithm used features such as shape of red blood cells Hough transform is introduced in this process. The result presented here is based on images with normal blood cells. The tested data consists of 10 samples and produced the accurate estimation rate closest to 96% from manual counting.
Authors and Affiliations
M. MumthajBegam, R. Geetha, A. Sagayaselvaraj
Keywords
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