Journal Article

Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias

Peregrina, Hayde, Gonzalez, Jesus A., Alonso, Jose E., Altamirano, Leopoldo, Reta, Carolina, Diaz-Hernandez, Raquel, Olmos, Ivan, Lobato, Ruben

2015

Overview

Morphological identification of acute leukemia is a powerful tool used by hematologists to determine the family of such a disease. In some cases, experienced physicians are even able to determine the leukemia subtype of the sample. However, the identification process may have error rates up to 40% (when classifying acute leukemia subtypes) depending on the physician's experience and the sample quality. This problem raises the need to create automatic tools that provide hematologists with a second opinion during the classification process. Our research presents a contextual analysis methodology for the detection of acute leukemia subtypes from bone marrow cells images. We propose a cells separation algorithm to break up overlapped regions. In this phase, we achieved an average accuracy of 95% in the evaluation of the segmentation process. In a second phase, we extract descriptive features to the nucleus and cytoplasm obtained in the segmentation phase in order to classify leukemia families and subtypes. We finally created a decision algorithm that provides an automatic diagnosis for a patient. In our experiments, we achieved an overall accuracy of 92% in the supervised classification of acute leukemia families, 84% for the lymphoblastic subtypes, and 92% for the myeloblastic subtypes. Finally, we achieved accuracies of 95% in the diagnosis of leukemia families and 90% in the diagnosis of leukemia subtypes.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Acute Disease / Algorithms / Analysis / Astrophysics / Blood / Bone marrow / Bone Marrow Cells - ultrastructure / Cancer / Classification / Computer science / Computers / Cytoplasm / Data mining / Diagnosis / Feature extraction / Hematology / Hospitals / Humans / Image classification / Image detection / Image Interpretation, Computer-Assisted - methods / Image processing / Image segmentation / International conferences / Leukemia / Leukemia - classification / Leukemia - diagnosis / Leukemia - pathology / Medical diagnosis / Medical personnel / Morphology / Nuclei / Pattern Recognition, Automated - methods / Pattern Recognition, Automated - statistics & numerical data / Physicians / Segmentation / Sensitivity and Specificity / Signal processing