Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms

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Autores
Rangayyan, Rangaraj M.
Nguyen, Thanh M.
Nandi, Asoke K.
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Citações na Scopus
Tipo de documento
Artigo Científico
Data
2010
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Primeira_Pagina_Artigo_2010_Effect_of_pixel_resolution_on_texture_features_of_breast_masses_in_mammograms_TC.pdf (62.39 KB)
ACESSO_RESTRITO_Artigo_2010_Effect_of_pixel_resolution_on_texture_features_of_breast_masses_in_mammograms_TC.pdf (174.8 KB)
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Resumo
The effect of pixel resolution on texture features computed using the gray-level co-occurrence matrix (GLCM) was analyzed in the task of discriminating mammographic breast lesions as benign masses or malignant tumors. Regions in mammograms related to 111 breast masses, including 65 benign masses and 46 malignant tumors, were analyzed at pixel sizes of 50, 100, 200, 400, 600, 800, and 1,000 μm. Classification experiments using each texture feature individually provided accuracy, in terms of the area under the receiver operating characteristics curve (AUC), of up to 0.72. Using the Bayesian classifier and the leave-one-out method, the AUC obtained was in the range 0.73 to 0.75 for the pixel resolutions of 200 to 800 μm, with 14 GLCM-based texture features using adaptive ribbons of pixels around the boundaries of the masses. Texture features computed using the ribbons resulted in higher classification accuracy than the same features computed using the corresponding regions within the mass boundaries. The t test was applied to AUC values obtained using 100 repetitions of random splitting of the texture features from the ribbons of masses into the training and testing sets. The texture features computed with the pixel size of 200 μm provided the highest average AUC with statistically highly significant differences as compared to all of the other pixel sizes tested, except 100 μm.

Palavras-chave
Breast cancer; Breast masses; Haralick's texture features; Mammography; Margins of masses; Pixel size; Pixel resolution; Ribbon around a mass; Texture analysis; Texture features; Tumor classification; Digital image processing; Image analysis; Mammography
Titulo de periódico
J Digit Imaging
URL da fonte
https://link.springer.com/article/10.1007/s10278-009-9238-0#citeas
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Idioma
Inglês
URL permanente
https://repositorio.insper.edu.br/handle/11224/7208
Notas
Membros da banca
Área do Conhecimento CNPQ
CIENCIAS DA SAUDE::MEDICINA::RADIOLOGIA MEDICA

CIENCIAS DA SAUDE::MEDICINA::CLINICA MEDICA::CANCEROLOGIA

CIENCIAS DA SAUDE::MEDICINA::RADIOLOGIA MEDICA

ENGENHARIAS::ENGENHARIA BIOMEDICA

CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA

CIENCIAS BIOLOGICAS::GENETICA::GENETICA HUMANA E MEDICA

CIENCIAS BIOLOGICAS::MORFOLOGIA::CITOLOGIA E BIOLOGIA CELULAR
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Coleção de Artigos Acadêmicos
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