Leukemia identification from bone marrow cells images using a machine vision and data mining strategy
The morphological analysis of medical images to support medical diagnosis is an important research area. This is the case of leukemia identification from bone marrow smears in which cells morphology is studied in order to classify the disease into its main family and subtype, so that a proper treatm...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2011 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | inglés |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/1603 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1603 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Acute leukemia classification/Acute leukemia classification info:eu-repo/classification/Cells images/Cells images info:eu-repo/classification/Data mining/Data mining info:eu-repo/classification/Machine vision/Machine vision info:eu-repo/classification/Feature extraction/Feature extraction info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Sumario: | The morphological analysis of medical images to support medical diagnosis is an important research area. This is the case of leukemia identification from bone marrow smears in which cells morphology is studied in order to classify the disease into its main family and subtype, so that a proper treatment can be indicated to the patient. In this paper we present a method to identify leukemia from bone marrow cells images using a combined machine vision and data mining strategy. Our process starts with a segmentation method to obtain leukemia cells and extract from them descriptive characteristics (geometrical, texture, statistical) and eigenvalues. We use these attributes to feed machine learning algorithms that learn to classify acute leukemia families and subtypes according to the FAB system. We show how the combination of descriptive features and eigenvalues helps to improve classification accuracy. Our method achieved accuracy above 95.5% to distinguish between the acute myeloblastic and lymphoblastic leukemia families and accuracy of 90% (and above) among five leukemia subtypes (after the acute leukemia families classification). |
|---|