Quantitative microscopy approach for shape-based erythrocytes characterization in anaemia.
J Microsc
; 249(2): 136-49, 2013 Feb.
Article
em En
| MEDLINE
| ID: mdl-23252834
ABSTRACT
Anaemia is one of the most common diseases in the world population. Primarily anaemia is identified based on haemoglobin level; and then microscopically examination of peripheral blood smear is required for characterizing and confirmation of anaemic stages. In conventional approach, experts visually characterize abnormality present in the erythrocytes under light microscope, and this evaluation process is subjective in nature and error prone. In this study, we have proposed a methodology using machine learning techniques for characterizing erythrocytes in anaemia associated with anaemia using microscopic images of peripheral blood smears. First, peripheral blood smear images are preprocessed based on grey world assumption technique and geometric mean filter for reducing unevenness of background illumination and noise reduction. Then erythrocyte cells are segmented using marker-controlled watershed segmentation technique. The erythrocytes in anaemia, such as, tear drop, echinocyte, acanthocyte, elliptocyte, sickle cells and normal erythrocytes cells have been characterized and classified based on their morphological changes. Optimal subset of features, ranked by information gain measure provides highest classification performance using logistic regression classifier in comparison with other standard classifiers.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Técnicas de Laboratório Clínico
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Eritrócitos
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Anemia
/
Microscopia
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Microsc
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
Índia