Comput Math Methods Med - Computer aided detection of breast density and mass, and visualization of other breast anatomical regions on mammograms using graph cuts.

Tópicos

{ cancer(2502) breast(956) screen(824) }
{ imag(2675) segment(2577) method(1081) }
{ research(1085) discuss(1038) issu(1018) }
{ studi(1119) effect(1106) posit(819) }
{ imag(1947) propos(1133) code(1026) }
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{ system(1976) rule(880) can(841) }
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{ imag(1057) registr(996) error(939) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
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{ gene(2352) biolog(1181) express(1162) }
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{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
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{ survey(1388) particip(1329) question(1065) }
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{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }

Resumo

Breast cancer mostly arises from the glandular (dense) region of the breast. Consequently, breast density has been found to be a strong indicator for breast cancer risk. Therefore, there is a need to develop a system which can segment or classify dense breast areas. In a dense breast, the sensitivity of mammography for the early detection of breast cancer is reduced. It is difficult to detect a mass in a breast that is dense. Therefore, a computerized method to separate the existence of a mass from the glandular tissues becomes an important task. Moreover, if the segmentation results provide more precise demarcation enabling the visualization of the breast anatomical regions, it could also assist in the detection of architectural distortion or asymmetry. This study attempts to segment the dense areas of the breast and the existence of a mass and to visualize other breast regions (skin-air interface, uncompressed fat, compressed fat, and glandular) in a system. The graph cuts (GC) segmentation technique is proposed. Multiselection of seed labels has been chosen to provide the hard constraint for segmentation of the different parts. The results are promising. A strong correlation (r = 0.93) was observed between the segmented dense breast areas detected and radiological ground truth.

Resumo Limpo

breast cancer most aris glandular dens region breast consequ breast densiti found strong indic breast cancer risk therefor need develop system can segment classifi dens breast area dens breast sensit mammographi earli detect breast cancer reduc difficult detect mass breast dens therefor computer method separ exist mass glandular tissu becom import task moreov segment result provid precis demarc enabl visual breast anatom region also assist detect architectur distort asymmetri studi attempt segment dens area breast exist mass visual breast region skinair interfac uncompress fat compress fat glandular system graph cut gc segment techniqu propos multiselect seed label chosen provid hard constraint segment differ part result promis strong correl r observ segment dens breast area detect radiolog ground truth

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