Classification and Segregation of Abnormal Lymphocytes through Image Mining for Diagnosing Rheumatoid Arthritis Using Min-max Algorithm

Advances in the acquisition of complex medical images and storing it for further analysis through image mining have significantly helped to identify the root causes for various diseases. Mining of medical image data set such as scanned images or blood cell images require extraction of implicit knowledge from the data set through hierarchical image processing techniques and identifying the relationships and patterns that are not explicitly stored in a single image. Rheumatoid Arthritis (RA) is an autoimmune disease and it cause chronic inflammation of the joints. Causes of the RA is unknown due to that need to find out in the early stage is required. Diagnosis of RA based on blood cell types and shapes requires computational analysis. An assistive technology for the doctor to detect and investigate rheumatoid arthritis is therefore required. The objective of the proposed work is to analyze the shapes of lymphocytes, a key component of blood cells that causes RA complications, to automate the process of identifying abnormal lymphocytes by estimating the centroids of lymphocytes using AIT centroid technique and thereby finding a differential count. The process involves cropping nucleus from the blood cell image, segmenting it and to investigate further whether the shapes of the lymphocytes are irregular and dissimilar. Features are extracted from each cell components for comparison and the abnormal lymphocytes are segregated from the normal. To enhance the segregation process, neural network based perceptron classifier tool is used.