Characterization of hematologic malignancies based on discrete orthogonal moments

During the last decade leukemia and lymphomas have been a hot topic in the biomedical area. Their diagnosis is a time-consuming task that, in many cases, delays treatments. On the other hand, discrete orthogonal moments (DOMs) are a tool recently introduced in biomedical image analysis. Here, we propose a combination of DOMs to help in the diagnosis of leukemia and lymphomas. We classify the IICBU2008-lymphoma dataset that includes three hematologic malignancies: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. Our methodology analyzes such diseases in the hema-toxylin and eosin color space. We also include feature analysis to preserve the most discriminating characteristics of the malignant tissues. Finally, the classification of the samples is performed with kernel Fisher discriminant analysis. The accuracy is 93.85%. The results show the proposal could be useful in different biomedical applications.