Comparison of colour spaces for optic disc localisation in retinal images

Osareh, Alireza; Mirmehdi, Majid; Thomas, Barry; Markham, R.H.

doi:10.1109/icpr.2002.1044865

Cited by 111 publications

(62 citation statements)

References 3 publications

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“…By using the data from various sources, this ensures that the data are more diverse, hence, testing the system to its full potential. As the optic disc has visually similar characteristics to those of bright lesions, an optic disc localisation algorithm [17] is first used to automatically mark out the location so as not to be detected as false positives.…”

Section: Resultsmentioning

confidence: 99%

Filtering normal retinal images for diabetic retinopathy screening using multiple classifiers

Goh

Tang

Saleh

et al. 2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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Abstract-Diabetic retinopathy is a complication of diabetes and early detection is essential for effective treatment. In this paper, a novel technique for the separation of normal and abnormal retinal images is described. Various features are extracted from local sub images and then fed through multiple classifiers to categorise them into interim classes followed by a reasoning process to give a more reliable and robust result. This is then followed by a global analysis to decide the normality of the whole image.

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Section: Resultsmentioning

confidence: 99%

Filtering normal retinal images for diabetic retinopathy screening using multiple classifiers

Goh

Tang

Saleh

et al. 2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

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“…With regard to segmentation methods based on deformable model, Osareh et al [10] proposed a template based method for location of OD center and for OD boundary extraction used initialization of snake on a OD region which was enhanced using morphological methods. Lowell used template matching for locating OD and for OD segmentation he used deformable contour model.…”

Section: Literature Surveymentioning

confidence: 99%

Comparison of Pre-Processing Methods for Segmentation and Approximation of Optic Disc Boundary From Processed Digital Retinal Images

Vaidya¹,

Doiphode²

2014

Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing 2014

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Algorithms are presented for rapid, automatic and accurate segmentation and Approximation of Optic Disc boundary from digital retinal images. This paper presents method that improves upon prior work in different ways; 1.Accuracy 2.Contrast. The pre-processing methods are presented here to enhance the image properties prior to the segmentation of OD from digital fundus images of retina using template based methodology. This paper compares performance of contrast enhancement techniques such as Intensity thresholding, adaptive histogram equalization and histogram equalization. Further, evaluates performance of each of these techniques with respect to the original template based OD segmentation using circular Hough Transform. Intensity thresholding method provides better performance with Maximum Variance method and Low Pass Filter method of ODP location. Histogram Equalization performs better with only Low Pass Filter method of ODP location. Adaptive Histogram Equalization performs better with all three methods of OD pixel location so ultimately it performs better with voting type algorithm that is used to locate the centroid of the pixel location detected by Maximum Difference; Maximum Variance and Low Pass Filter method. Thus the optic disc segmentation performance is improved over the original method. Experimental results on a known MESSIDOR database and local NIOP database, achieving to more than 93% accuracy for optic disc boundary approximation. it proved that adaptive histogram performs better as a contrast enhancement technique.

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“…As the proposed technique makes use of the circular brightness structure of the OD, the lightness component of a retinal image is first extracted. We use the lightness component within the LAB color space, where the OD detection usually performs the best [23]. For the retinal image in Fig.…”

Section: Retinal Image Pre-processingmentioning

confidence: 99%

An Automatic Screening Method to Detect Optic Disc in the Retina

Murugan¹

2012

IJAIT

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The location of Optic Disc (OD) is of critical importance in retinal image analysis. This research paper carries out a new automated methodology to detect the optic disc (OD) in retinal images. OD detection helps the ophthalmologists to find whether the patient is affected by diabetic retinopathy or not. The proposed technique is to use line operator which gives higher percentage of detection than the already existing methods. The purpose of this project is to automatically detect the position of the OD in digital retinal fundus images. The method starts with converting the RGB image input into its LAB component. This image is smoothed using bilateral smoothing filter. Further, filtering is carried out using line operator. After which gray orientation and binary map orientation is carried out and then with the use of the resulting maximum image variation the area of the presence of the OD is found. The portions other

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Comparison of colour spaces for optic disc localisation in retinal images

Cited by 111 publications

References 3 publications

Filtering normal retinal images for diabetic retinopathy screening using multiple classifiers

Filtering normal retinal images for diabetic retinopathy screening using multiple classifiers

Comparison of Pre-Processing Methods for Segmentation and Approximation of Optic Disc Boundary From Processed Digital Retinal Images

An Automatic Screening Method to Detect Optic Disc in the Retina

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