Review of Preprocessing Techniques for Fundus Image Analysis

Joshi, Shilpa; Karule, P. T.

doi:10.18280/ama_b.600306

Cited by 4 publications

(3 citation statements)

References 113 publications

(102 reference statements)

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“…The distractions in this acquisition can affect the crack detection process. Such issues are reduced by preprocessing [23].…”

Section: Preprocessing Methodsmentioning

confidence: 99%

Efficient Crack Severity Level Classification Using Bilayer Detection for Building Structures

Anitha¹,

Hemalatha²

2023

Computer Systems Science and Engineering

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Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures. Moreover, identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings. Hence, this paper proposes an efficient strategy to classify the cracks into fine, medium, and thick using a novel bilayer crack detection algorithm. The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification. The proposed algorithm works well in the dark background and connects the discontinued cracks too. The first layer is used to detect cracks under texture variations and manufacturing defects, through segmented adaptive thresholding and morphological operations. The residual noise present in the output of the first layer is removed in the second layer of crack detection. The second layer includes the double scan and the noise reduction algorithms and is used to join the missed crack parts. As a result, a segmented crack is formed. Further classification is done using an ensemble classifier with bagging, and decision tree techniques by extracting the geometrical features and the weaker crack criterion from the segmented part. The results of the proposed technique are compared with the existing techniques for different datasets and have obtained a rise in True Positive Rate (TPR), accuracy and precision value. The proposed technique is also implemented in Raspberry Pi for further real-time evaluation.

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“…The distractions in this acquisition can affect the crack detection process. Such issues are reduced by preprocessing [23].…”

Section: Preprocessing Methodsmentioning

confidence: 99%

Efficient Crack Severity Level Classification Using Bilayer Detection for Building Structures

Anitha¹,

Hemalatha²

2023

Computer Systems Science and Engineering

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“…Mathematical morphology in digital image processing is an appropriate tool to analyze shapes in images, which involves two basic processes: erosion and dilation [33]. Dilation is employed to increase the size of the object segment by adding layers around the object.…”

Section: Morphological Operationsmentioning

confidence: 99%

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

Firdausy

Wahyunggoro²,

Nugroho³

et al. 2022

Int. J. Adv. Intell. Informatics

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The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques

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“…Under the situation of scarcity of data in the target domain, the performance of the traditional agent simulation model tends to decrease. In this scenario, the useful knowledge in the source domain is extracted to guide the target domain learning to obtain more appropriate class information and agent simulation performance is an effective learning method [4]. The patient exposed in X-ray radiation which may create the biological effects like cancer hereditary effects and noncancer effect like respiration diseases, stroke and digestive disorders.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for computer tomography in low radiation images

Sathishkumar¹,

Nagarajan²

2018

AMA_B

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Computer Tomography (CT) is one of the efficient imaging techniques employed in medical field for the past few decades. In CT Scanning, image quality has influenced by several factors like noise, slice thickness, minimum and maximum contrast resolution, radiation dose etc., Radiation dose is one among important and challenging issues taken to optimize the reconstruction algorithm in CT. The radiation dose is controlled by tube current*time product (mAs), pitch or table speed, slice thickness, beam energy (kVp) and number of patients. In this paper, Landweber algorithm is used to check the improvement in quality of image in low radiation dose. The projected algorithm is compared with existing iterative reconstruction algorithm in Test Phantom, Head image and Thorax image and shows better results. The proposed methods will be useful to optimize an iterative reconstruction algorithm with adequate level of quality in computer tomography.

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Review of Preprocessing Techniques for Fundus Image Analysis

Cited by 4 publications

References 113 publications

Efficient Crack Severity Level Classification Using Bilayer Detection for Building Structures

Efficient Crack Severity Level Classification Using Bilayer Detection for Building Structures

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

An efficient algorithm for computer tomography in low radiation images

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