Comput. Biol. Med. - Fully automated gradient based breast boundary detection for digitized X-ray mammograms.

Tópicos

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{ imag(2675) segment(2577) method(1081) }
{ detect(2391) sensit(1101) algorithm(908) }
{ cancer(2502) breast(956) screen(824) }
{ use(2086) technolog(871) perceiv(783) }
{ model(2656) set(1616) predict(1553) }
{ drug(1928) target(777) effect(648) }
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{ clinic(1479) use(1117) guidelin(835) }
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{ method(1557) propos(1049) approach(1037) }
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{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
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{ data(3963) clinic(1234) research(1004) }
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{ perform(999) metric(946) measur(919) }
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{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }

Resumo

Accurate segmentation of the breast from digital mammograms is an important pre-processing step for computerized breast cancer detection. In this study, we propose a fully automated segmentation method. Noise on the acquired mammogram is reduced by median filtering; multidirectional scanning is then applied to the resultant image using a moving window 15?1 in size. The border pixels are detected using the intensity value and maximum gradient value of the window. The breast boundary is identified from the detected pixels filtered using an averaging filter. The segmentation accuracy on a dataset of 84 mammograms from the MIAS database is 99%.

Resumo Limpo

accur segment breast digit mammogram import preprocess step computer breast cancer detect studi propos fulli autom segment method nois acquir mammogram reduc median filter multidirect scan appli result imag use move window size border pixel detect use intens valu maximum gradient valu window breast boundari identifi detect pixel filter use averag filter segment accuraci dataset mammogram mia databas

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