Detection of exudates in retinal images using a pure splitting technique

Jaafar, Hussain F.; Nandi, Asoke K.; Al-Nuaimy, Waleed

doi:10.1109/iembs.2010.5626014

Cited by 55 publications

(42 citation statements)

References 11 publications

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“…A local variation operation to outline boundaries and then split and merge algorithm is used to extract all bright candidates locally. But sometimes it extracts false structures [32]. In 2007 Xuejing et al proposed two strategies for image segmentation based on SM method [33].…”

Section: Split and Mergementioning

confidence: 99%

Split and Merge: A Region Based Image Segmentation

Bala¹,

Sharma²

2018

IJERMT

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Abstractmage segmentation is a very challenging task in digital image processing field. It is defined as the process of takeout objects from an image by dividing it into different regions where regions that depicts some information are called objects. There are different types of image segmentation algorithms. The segmentation process depends upon the type of description required for an application for which segmentation is to be performed. Hence, there is no universally accepted segmentation algorithm. Region segmentation is divided into three categories region growing, split and merge and watershed. But this study confines only to split and merge techniques. This paper includes split and merge approaches and their extended versions. This study highlights the main limitations and potentials of these approaches.

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Section: Split and Mergementioning

confidence: 99%

Split and Merge: A Region Based Image Segmentation

Bala¹,

Sharma²

2018

IJERMT

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“…Jafaar et al [5] segment images with logical intersection of coarse segmentation based on local variation, and the results of adaptive thresholding based on combination of pure splitting and histogram-based thresholding. The coarse segmentation assumes that hard exudates have clear boundaries, and is based on the automatic thresholding of a standard deviation image using Otsu's method, followed by classification of features such as major axis length, minor axis length, area and solidity.…”

Section: Related Workmentioning

confidence: 99%

Refining Coarse Manual Segmentations with Stable Probability Regions

Laaksonen

Herttuainen

Uusitalo

et al. 2015

Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop

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Abstract. Most feature-based lesion detection and computer-aided diagnosis methods for medical images require representative data of each region of interest (ROI) for parameter selection. Furthermore, the spatial accuracy of the segmentation of the ROIs from the background can significantly affect certain image features extracted from the ROIs. However, requiring spatially accurate manual segmentations of the ROIs to be used as the ground truth is infeasible for large image sets due to the amount of manual work involved. To relax the requirement of spatial accuracy and to enable spatial refinement of coarse manual segmentations to have more representative feature data, a method based on color information and maximally stable extremal regions of lesion likelihoods is presented. The proposed method is quantitatively compared to several segmentation approaches by using a challenging set of retinal images with spatially accurate ground truth of exudates. The experiments show that the proposed method produces good results measured as Dice coefficients between the refined segmentation and ground truth.

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“…The processing of these images starts by the segmentation of the retinal disk and removing the noisy background that can affect the subsequent features (e.g. optic disk, vessels) extraction (Jaafar, Nandi et al 2010, Akram 2012). …”

Section: Medical Fundus Imagesmentioning

confidence: 99%

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

Hayajneh¹,

Rashad²,

Saraereh³

et al. 2016

CIS

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The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process. The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

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Detection of exudates in retinal images using a pure splitting technique

Cited by 55 publications

References 11 publications

Split and Merge: A Region Based Image Segmentation

Split and Merge: A Region Based Image Segmentation

Refining Coarse Manual Segmentations with Stable Probability Regions

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

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