An Approach to Detect Hard Exudates Using Normalized Cut Image Segmentation Technique in Digital Retinal Fundus Image

Kayal, Diptoneel; Banerjee, Sreeparna

doi:10.1007/978-3-642-30157-5_13

Cited by 11 publications

(7 citation statements)

References 8 publications

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“…The recommended weights k i j and the parameters σ i j are given in [37]. In (16), j ∈ [1, 3] for i = 1, and j ∈ [1,2] for i ∈ [2,3]. The blurring effect of each Gaussian function in (16) is represented by the product of the spread, σ i j , expressed in degrees of visual angle, and the number of pixels per degree of visual angle when the image is observed from a given distance, d [38].…”

Section: S-cielabmentioning

confidence: 99%

“…It has been used in different applications [8,[13][14][15][16][17] and many variants have been presented [18][19][20][21][22][23]. In these works, the parameters required to compute the pixel-to-pixel similarity as well as the stopping condition were manually established for each and every image, a process that strongly diminishes the method's usability and makes the reproduction of their algorithms difficult.…”

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confidence: 99%

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Normalized Cut optimization based on color perception findings. A comparative study

Sáez

Serrano

Acha

2014

Machine Vision and Applications

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This paper proposes a methodology to obtain a fully automatic color segmentation algorithm based on the Normalized Cut (Ncut) proposed by Shi and Malik, using recent findings in color perception. A weighting matrix computed using a perceptually uniform color space (CIE L * a * b * ) and color distance formulae correlated with the visually perceived color differences (CIE94 and CIEDE2000); a stopping condition related to perceptual criteria; an automatic parameters setting required to compute the affinity matrix are proposed. To test the proposed methodology, a wide study about the influence of the color space choice, different stopping conditions, and different similarity measurements is carried out. These alternatives are exhaustively evaluated using perception-related measurements (S-CIELAB) and general segmentation evaluation metrics applied to the 500 images of the Berkeley database. The results showed that the proposed method outperforms Ncut based on other color spaces, similarity measure or stopping conditions. Furthermore, the usability of the method is increased by replacing the manual parameter setting for an automatic.

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Section: S-cielabmentioning

confidence: 99%

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confidence: 99%

Normalized Cut optimization based on color perception findings. A comparative study

Sáez

Serrano

Acha

2014

Machine Vision and Applications

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“…Image subtraction is used to find changes between two images of same scene. As per literature image subtraction can be mathematically denoted as follows [14]:…”

Section: Image Subtractionmentioning

confidence: 99%

“…As mentioned earlier median filtering makes the brighter regions (i.e. exudates) into blur, hence the result of subtraction gives us the output in which only regions with high brightness and contrast can be observed [14]. Subtraction is one of the most important step of this process as in this step the desired features of input retinal fundus image is extracted [14].…”

Section: Image Subtractionmentioning

confidence: 99%

A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

Banerjee

Kayal

2014

IJCT

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Diabetic retinopathy is considered to be one of the major causes of blindness among diabetes mellitus patients. Due to diabetic retinopathy blood vessels of retina gets damaged and fat, lipoprotein substances gets leaked out of the damaged blood vessels and are deposited in the intra retinal space. These substances are viewed as yellowish or whitish in color and are termed as exudates. They are the most important visible sign of the presence of diabetic retinopathy. Exudates are of two types, (a) hard exudates and (b) soft exudates. If the disease is not detected in early stages then it may lead to complete loss of vision to the diabetes patients. Detection of exudates is extremely difficult to detect by visual inspection due to small inner diameter of retina and inadequate lighting conditions. An efficient image analysis program can detect the presence effectively. In this paper we have proposed an automatic method for detection of hard exudates. The proposed method exhibits a sensitivity of 97.60% and specificity of 93% and accuracy of 95.70%.

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“…These include thresholding and edge detection based techniques [2], FCM based approach [3], gray level variation based approach [4], multilayer perceptron based approach [5]. Normalized cut image segmentation based method for detection of hard exudates has also been proposed by us [6]. Optic disk must be detected and segmented early in the detection process, as often optic disk has more or less same brightness and contrast as the exudates.…”

Section: Introductionmentioning

confidence: 99%

Detection of Hard Exudates Using Simulated Annealing Based Thresholding Mechanism in Digital Retinal Fundus Image

Kayal¹,

Banerjee²

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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An Approach to Detect Hard Exudates Using Normalized Cut Image Segmentation Technique in Digital Retinal Fundus Image

Cited by 11 publications

References 8 publications

Normalized Cut optimization based on color perception findings. A comparative study

Normalized Cut optimization based on color perception findings. A comparative study

A Novel Way to Detect Hard Exudates Using Dynamic Thresholding Technique in Digital Retinal Fundus Image

Detection of Hard Exudates Using Simulated Annealing Based Thresholding Mechanism in Digital Retinal Fundus Image

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