Automatic Localization and Boundary Detection of Optic Disc Using Implicit Active Contours

Siddalingaswamy, P. C.; Prabhu, K. Gopalakrishna

doi:10.5120/171-298

Cited by 48 publications

(26 citation statements)

References 12 publications

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“…The automatic and well-organized detection of the position of the OD in colour retinal images is an significant and basic step in the automated retinal image analysis system [7], [8].To successfully discover abnormal structures in a retinal image, it is often essential to mask out the usual anatomy from the analysis. An example of this is the OD, an anatomical structure with a bright outward show, which should be ignored when detecting bright lesions.…”

Section: Review Of Methodsmentioning

confidence: 99%

Localization of Optic Disc and Blood Vessel Segmentation: A Survey

Bharathan¹,

Anjali²,

Remya³

2017

IJCA

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Nowadays, number of retinal disorders are affecting to all age groups. Various research activities are being carried out to diagnosis the disorders. Damage to retina leads to loss of vision. An efficient detection of optic disc(OD) is an important task in an automated retinal image examination system. Most of the OD detection algorithms are developed for normal retinal images. So, to detect OD from normal, healthy and abnormal retinal images are challenging tasks. In this work, the various methods used to locate OD and blood vessel segmentation from retinal images are discussed.

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Section: Review Of Methodsmentioning

confidence: 99%

Localization of Optic Disc and Blood Vessel Segmentation: A Survey

Bharathan¹,

Anjali²,

Remya³

2017

IJCA

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“…Dehghani et al (2012) presented a new approach for localising the optic disc and has sugessted that future research can be carried with optic disc for human recognition. A new approach to automatically locate and detect optic disc boundary was presented by (Siddalingaswamy and Prabhu, 2010). Later a new method for automatic detection of optic disc and other bright lesions, such as hard exudates and cotton wool spots in colour fundus images was presented by (Ahmed et al, 2011).…”

Section: Related Work and Motivationmentioning

confidence: 99%

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

Malathy

Sadiq²,

Annapurani

2015

Journal of Computer Science

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Abstract:Person identification based on unimodal biometric system suffers from noise, intra class similarity, non universality, distinctiveness and spoof attacks. To alleviate the problem faced in unimodal biometric system biometric traits are combined in multimodal biometric system. In this study a new approach, to improve the recognition rate, reduces computational complexity and storage space is presented. Distinct method of person identification using detection and extraction of optic disc from retina and concha from ear is carried out Region Of Interest (ROI) locator which is proposed here automatically detects the optic disc either from right or left eye and extracts it. Feature level fusion of optic disc and concha is done for recognition of a person. The method is tested with ROI locator and without ROI locator on publicly available databases and the experimental result shows that our multimodal biometric system outperforms with ROI locator than Without ROI locator. Matching Rate (MR) of 95 to 100% and Equal Error Rate (EER) of less than 10% is achieved with this system. The new approach was tested for unimodal system with ROI locator and was able to achieve 100% Matching Rate.

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“…Optic disc detection is required as a prerequisite for the subsequent stages in many methods applied for identification of the pathological structures in retinal images. For example, in blood vessel tracking approaches the position of vessels in the neighborhood of optic disc is used as seeds for vessel tracking [4]. The process of automatically detecting/localizing the OD aims only to correctly detect the centroid (center point) of the OD.…”

Section: Introductionmentioning

confidence: 99%

“…Digital photography of the retina is widely used for screening of patients suffering from sight threatening diseases such as Diabetic retinopathy and Glaucoma. The timely diagnosis and referral for management of these diseases can prevent 98% of severe visual loss [4]. Moreover, automated approaches will be much more precise and consistent in phenol typing of retinal diseases such as Age-related macular degeneration [18].…”

Section: Introductionmentioning

confidence: 99%

Development of Algorithm for Dual Stage Classification to Estimate Severity Level of Diabetic Retinopathy in Retinal Images using Soft Computing Techniques

Prakash¹,

Selvathi²,

Hemalakshmi³

2014

ijeei

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Diabetic Retinopathy is a general origin of sight-threatening complication to which the condition occurring in persons with diabetes and which makes progressive damage to the retina. Retina is a back of eye with the light sensitive lining. The severity of this Diabetic Retinopathy in retina has four stages. The identification of the severity level of Diabetic Retinopathy in retina is a critical task. In order to make ease this task, a severity analysis method is proposed in this paper. The proposed method contains the 5 stages -(1) Pre-processing phase (2) Segmentation Phase (3) Feature Extraction Phase (4) Classification Phase I and (5) Classification Phase II. The retinal images are subjected to pre-processing phase for removing the noises from the image and make clear visible image of retina. Then the optic disks and the blood vessels in the retina are segmented using Modified Region Growing method and morphological operations, respectively. Before analyzing the severity in retinal images, it is necessary to classify the images into normal and abnormal images using Neural Network classifier. For this classification of images, the features mean, variance, entropy and area are extracted from the segmented optic disk of retinal images and also the features mean, variance, entropy, area, diameter and number of regions are extracted from the segmented blood vessels of retinal images. From the abnormal images, the severity of Diabetic Retinopathy can be evaluated by using SVM classifier based on area and intensity level of Hard Exudates and Hemorrhages. These techniques are implemented on publicly available database such as STARE and on real datasets using MATLAB 7.12. The performance of our proposed method is analyzed by Sensitivity, Specificity and Accuracy. From the results, it is proved that our work outperforms other works by providing very much better accuracy by classifying the severity level in Diabetic Retinopathy.

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Automatic Localization and Boundary Detection of Optic Disc Using Implicit Active Contours

Cited by 48 publications

References 12 publications

Localization of Optic Disc and Blood Vessel Segmentation: A Survey

Localization of Optic Disc and Blood Vessel Segmentation: A Survey

An Innovative Approach for Person Identification by Detection and Extraction of Optic Disc from Retina and Concha from Ear

Development of Algorithm for Dual Stage Classification to Estimate Severity Level of Diabetic Retinopathy in Retinal Images using Soft Computing Techniques

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