Classification of chronic myeloid leukemia neutrophils by hyperspectral imaging using Euclidean and Mahalanobis distances

Abstract

Chronic Myeloid Leukemia (CML) is a type of blood cancer which needs to be diagnosed in early stages to facilitate effective treatment. This necessitates quick, error free and automated diagnostic techniques. In this study, hyperspectral images have been analyzed using statistical distances to classify neutrophils from CML versus healthy blood samples. The statistical distances were used in multidimensional space offered by hyperspectral images. For computational efficiency, principal component analysis was used to achieve dimensionality reduction. The Euclidean distance method, and Mahalanobis distance method which compensates the variance of the target data distribution were used to classify CML neutrophils. The effectiveness of the proposed methods were tested and compared using experimental results. The Euclidean distance was found to be superior when it came to sensitivity in detecting CML neutrophils whereas the Mahalanobis distance was better at detecting healthy neutrophils and distinguishing CML neutrophils from healthy neutrophils.