AUTOMATIC SICKLE CELL ANEMIA DETECTION USING IMAGE PROCESSING TECHNIQUE

One of the important parts of the human body is red blood cells (RBCs). Disk shape is the ordinary red blood cell's shape. One type of ailment of blood is sickle cell anemia (SCA) in where red blood cells are formed in crescent shapes from their actual shapes. Thousands of babies around the world are born with this blood disorder every year. The numbers of SCA are assumed to increase about 30% by 2050 globally. About 0.07 to 0.1 million Americans are victims of SCA. A new way is introduced to detect and classify sickle cells in RBC using image processing technique to start treatment as early as possible. Firstly, this method collects images of blood. The pre-processing phase is done through gray scale image conversion, image enhancement & median filter. Then, the threshold segmentation is applied to segment the RBCs and morphological operations are used to remove the undesired objects from images. Metric value, aspect ratio, entropy, mean, standard deviation and variance are used as features which are extracted. Finally, the support vector machine classifier is trained to test the images. The system provided better accuracy and sensitivity than existing methods. This automatic detection technique would be very useful to save the precious lives of the people.