J Med Syst - An improved retinal vessel segmentation method based on high level features for pathological images.

Tópicos

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{ method(1557) propos(1049) approach(1037) }
{ patient(2315) diseas(1263) diabet(1191) }
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{ signal(2180) analysi(812) frequenc(800) }
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{ method(2212) result(1239) propos(1039) }

Resumo

Most of the retinal blood vessel segmentation approaches use low level features, resulting in segmenting non-vessel structures together with vessel structures in pathological retinal images. In this paper, a new segmentation method based on high level features is proposed which can process the structure of vessel and non-vessel independently. In this method, segmentation is done in two steps. First, using low level features segmentation is accomplished. Second, using high level features, the non-vessel components are removed. For evaluation, STARE database is used which is publicly available in this field. The results show that the proposed method has 0.9536 accuracy and 0.0191 false positive average on all images of the database and 0.9542 accuracy and 0.0236 false positive average on pathological images. Therefore, the proposed approach shows acceptable accuracy on all images compared to other state of the art methods, and the least false positive average on pathological images.

Resumo Limpo

retin blood vessel segment approach use low level featur result segment nonvessel structur togeth vessel structur patholog retin imag paper new segment method base high level featur propos can process structur vessel nonvessel independ method segment done two step first use low level featur segment accomplish second use high level featur nonvessel compon remov evalu stare databas use public avail field result show propos method accuraci fals posit averag imag databas accuraci fals posit averag patholog imag therefor propos approach show accept accuraci imag compar state art method least fals posit averag patholog imag

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