Wednesday, 26 February 2020

MATLAB Code of Sickle Cell Disease Detection using Red Blood Cell Cluster Separation

Today I would like to post one MATLAB Project which is based on Biomedical Image Processing. Now a days Biomedical Image Processing gains much importance in the field of Digital Image Processing.  Red Blood Corpuscles are the major cellular component of human blood which are responsible for gaseous exchange between living cells and the external environment. In normal physiological conditions, and RBC is circular in front view and bi-concave inside view. In terms of size, it is 7.5 μm in diameter and 2 μm in thickness. This normal morphology of RBC undergoes specific changes as a consequence of different pathological abnormalities. 

One of such disease is ‘Sickle Cell Anaemia’ where the RBCs take crescentic ‘sickle’ like shape. Here in this paper, the correct identification of aberration in normal parameters of RBCs in an anaemic blood sample has been presented using different image processing tools and techniques. Here some preprocessing is done using the Weiner filter and the Sobel Edge detection method is used to find the boundary of the corpuscles. Then using region properties, a metric is formulated to determine the abnormal shape of the corpuscles to diagnose the disease.

Figure: Normal Blood Cells and Sickle Cells

Above figure shows us the difference between normal RBC cells and the Sickle cells. Our aim is to determine Sickle cells from blood by using various image processing techniques.

MATLAB Implementation:

Figure: Blank GUI

Figure: GUI of Sickle Cell Detection

YouTube Video:

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