2010
DOI: 10.1016/j.compbiomed.2009.11.004
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Vascular intersection detection in retina fundus images using a new hybrid approach

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Cited by 48 publications
(45 citation statements)
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“…The method proposed here introduces a new technique which uses the graph representation of the segmented images and added extra features round the junction points to achieve better reliable classification performances. Both DRIVE and STARE were chosen in this work since they are popular publicly available datasets and a lot of previous and current works are using them (Aibinu et al, 2010;Azzopardi et al, 2011;Fathi et al, 2013), thus more comprehensive comparison between our method and those of other researchers will be achieved.…”
Section: Related Workmentioning
confidence: 99%
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“…The method proposed here introduces a new technique which uses the graph representation of the segmented images and added extra features round the junction points to achieve better reliable classification performances. Both DRIVE and STARE were chosen in this work since they are popular publicly available datasets and a lot of previous and current works are using them (Aibinu et al, 2010;Azzopardi et al, 2011;Fathi et al, 2013), thus more comprehensive comparison between our method and those of other researchers will be achieved.…”
Section: Related Workmentioning
confidence: 99%
“…Another method given by Azzopardi et al (2011) was used to identify the landmarks by an initial training phase to configure a bank of Gabor filters; yet, that method did not proceed to distinguish between bifurcations and crossovers. Aibinu et al (2010) used a new hybrid approach called the combined cross-point number method to detect the bifurcation and intersection points in the fundus images using a window of 5×5 pixels. Dashtbozorg et al (2014) proposed an automatic graph-based approach to classify the vessels into arteries and veins.…”
Section: Related Workmentioning
confidence: 99%
“…Only the intensity (I) matrix is saved for further processing. b) Fruit Image Segmentation and Boundary Extraction Fruit image segmentation can be described as the partitioning of the intensity image obtained from the RGB components image into two nonoverlapping, connected, groups of pixels (regions) [1], [3], [4]. Here the fruit image segmentation involves the use of Otsu method and this is followed by morphological operation of erosion and dilation [23].…”
Section: ) Fruit Shape Recognition Systemmentioning
confidence: 99%
“…and min(R, G, B) denotes the minimum of RGB components of the acquired input fruit image [1], [3], [4]. The choice of HSI color space is based on the ease of its conversion and its similarity to gray level image color space conversion.…”
Section: ) Fruit Shape Recognition Systemmentioning
confidence: 99%
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