title: Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias.
abstract: 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.
authors.affiliation: Department of Computer Science, National Institute for Astrophysics, Optics, and Electronics, Puebla, Mexico.
authors.forename: Carolina
authors.initials: C
authors.lastname: Reta
authors.affiliation: Department of Computer Science, National Institute for Astrophysics, Optics, and Electronics, Puebla, Mexico.
authors.forename: Leopoldo
authors.initials: L
authors.lastname: Altamirano
authors.affiliation: Department of Computer Science, National Institute for Astrophysics, Optics, and Electronics, Puebla, Mexico.
authors.forename: Jesus A
authors.initials: JA
authors.lastname: Gonzalez
authors.affiliation: Department of Computer Science, National Institute for Astrophysics, Optics, and Electronics, Puebla, Mexico.
authors.forename: Raquel
authors.initials: R
authors.lastname: Diaz-Hernandez
authors.affiliation: Department of Computer Science, National Institute for Astrophysics, Optics, and Electronics, Puebla, Mexico.
authors.forename: Hayde
authors.initials: H
authors.lastname: Peregrina
authors.affiliation: Faculty of Computer Science. Autonomous University of Puebla, Puebla, Mexico.
authors.forename: Ivan
authors.initials: I
authors.lastname: Olmos
authors.affiliation: Department of Hematology, Mexican Social Security Institute, Puebla, Mexico.
authors.forename: Jose E
authors.initials: JE
authors.lastname: Alonso
authors.affiliation: Department of Hematology, Mexican Social Security Institute, Puebla, Mexico.
authors.forename: Ruben
authors.initials: R
authors.lastname: Lobato
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D001854:Bone Marrow Cells / Q000648:ultrastructure*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007938:Leukemia / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity