This paper investigates automated diagnosis of red blood cell’s and describes a method to classify the different shapes of red blood cells using the image processing techniques. The shape of red blood cells can be analyzed and their structures can be recognizing with the help of image processing techniques.
Visual inspection of microscopic images is the most widely used technique for determination of different shapes of red blood cells and it is a labour-intensive repetitive and time consuming task. To classify the structure of red blood cells, edge detection and segmentation are the two image processing techniques used.
The image of red blood cells are captured through the microscope, plotted on the glass slide or recorded from the Scanning electron microscope. An image can be considered as a matrix of light intensity levels. This paper provides the way to classify the different structure of RBC with the help of different methods of image processing.
An image can be considered as a matrix of light intensity levels that can be manipulated using computer algorithms in MATLAB. Although none of the algorithms developed can be used, as of now, in a real time sense, they provide some insight into the feasibility of imaging processing techniques.
For image processing, the analysis must be carried out. Image analysis is concerned with the extraction of measurements, data or information from an image by automatic or semiautomatic methods. Image analysis is distinguished from other types of image processing, such as coding, restoration, and enhancement, in that the ultimate product of an image analysis system is usually numerical output rather than a picture. Image analysis also diverges from classical pattern recognition in that analysis systems.
The shape of red blood cell contributes more to clinical diagnosis with respect to blood diseases. The image processing is carried out with the help of segmentation, edge detection, edge smoothing.
Author: Navin D. Jambhekar