Comparison of Accuracy of Color Spaces in Cell Features Classification in Images of Leukemia types ALL and MM

Abstract

This study presents a methodology for identifying the color space that provides the best performance in an image processing application. When measurements are performed without selecting the appropriate color model, the accuracy of the results is considerably altered. It is significant in computation, mainly when a diagnostic is based on stained cell microscopy images. This work shows how the proper selection of the color model provides better characterization in two types of cancer, acute lymphoid leukemia, and multiple myeloma. The methodology uses images from a public database. First, the nuclei are segmented, and then statistical moments are calculated for class identification. After, a principal component analysis is performed to reduce the extracted features and identify the most significant ones. At last, the predictive model is evaluated using the k-nearest neighbor algorithm and a confusion matrix. For the images used, the results showed that the CIE L*a*b color space best characterized the analyzed cancer types with an average accuracy of 95.52%. With an accuracy of 91.81%, RGB and CMY spaces followed. HSI and HSV spaces had an accuracy of 87.86% and 89.39%, respectively, and the worst performer was grayscale with an accuracy of 55.56%.