Neonatal Blood Analysis and Generating Disorder Estimation Using Image Processing

The analysis of blood cells in microscopic images can provide useful information concerning the health of patients; however, manual classification of blood cells is time-consuming. Classification is susceptible to error due to the different morphological features of the cells. Therefore, a fast and automated method for identifying the different shape of blood cells is required. Neonatal blood cell diseases have grown in a large amount in the recent past. Acute pneumonia, leukemia and anemia being some of such diseases, they do have an impact on the blood cell size and its properties. Such diseases tend to change the size and shape of the RBCs, WBCs and Platelet, and also sometimes leading to change in their gross counts. The propose system help to work for analysis the changes by shape analysis & size detection of blood cell using image processing. The image is converted to gray scale image from further processing. The use of image processing discards the need of the expert pathologists as well as reduces the volume of blood required to be withdrawn from the neonatal baby. A novel shape detection base algorithm will be developed which can classify the neonatal diseases depending upon morphological changes in the blood cells. Also, this algorithm causes the reduction of noise in the final image that might completely discards the possibility of error, leading to accurate detection of blood cell problems.