Comput. Biol. Med. - Optic disc segmentation using the sliding band filter.

Tópicos

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Resumo

CKGROUND: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases.METHODS: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a downsampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm.RESULTS: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions.DISCUSSION: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view.

Resumo Limpo

ckground optic disc od centr boundari import landmark retin imag essenti autom calcul health biomark relat preval system disord diabet hypertens cerebrovascular cardiovascular diseasesmethod paper present automat approach od segment use multiresolut slide band filter sbf preprocess phase lowresolut sbf appli downsampl retin imag locat maxim filter respons use focus analysi reduc region interest roi highresolut sbf appli obtain set pixel associ maximum respons sbf give coars estim od boundari regular use smooth algorithmresult result compar manual extract boundari public databas onhsd messidor inspireavr dataset outperform recent approach od segment onhsd result classifi excel remain imag distribut good fair categori averag overlap area achiev imag onhsd messidor inspiravr dataset respect compar manual delin od regionsdiscuss evalu result imag three dataset demonstr better perform propos method compar recent publish od segment approach prove independ method chang imag characterist size qualiti camera field view

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