Edge-Detection in Noisy Images Using Independent Component Analysis

Mendhurwar, Kaustubha; Patil, Shivaji Govind; Sundani, Harsh; Aggarwal, Priyanka; Devabhaktuni, Vijay

doi:10.5402/2011/672353

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…We enhanced the contrast and normalized the images using zero mean and unit variance for each image. Then, we applied a Gaussian noise that is helpful to make the edges obvious [ 73 ]. Figure 2 shows the preprocessing stages, that change the appearance of the image, and this process is done for the whole dataset.…”

Section: Methodsmentioning

confidence: 99%

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Ahmad

Qadri

Ashraf

et al. 2022

Computational Intelligence and Neuroscience

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Segmentation of a liver in computed tomography (CT) images is an important step toward quantitative biomarkers for a computer-aided decision support system and precise medical diagnosis. To overcome the difficulties that come across the liver segmentation that are affected by fuzzy boundaries, stacked autoencoder (SAE) is applied to learn the most discriminative features of the liver among other tissues in abdominal images. In this paper, we propose a patch-based deep learning method for the segmentation of a liver from CT images using SAE. Unlike the traditional machine learning methods, instead of anticipating pixel by pixel learning, our algorithm utilizes the patches to learn the representations and identify the liver area. We preprocessed the whole dataset to get the enhanced images and converted each image into many overlapping patches. These patches are given as input to SAE for unsupervised feature learning. Finally, the learned features with labels of the images are fine tuned, and the classification is performed to develop the probability map in a supervised way. Experimental results demonstrate that our proposed algorithm shows satisfactory results on test images. Our method achieved a 96.47% dice similarity coefficient (DSC), which is better than other methods in the same domain.

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Section: Methodsmentioning

confidence: 99%

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Ahmad

Qadri

Ashraf

et al. 2022

Computational Intelligence and Neuroscience

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“…We incorporated Independent Component Analysis (ICA) based threshold optimization to obtain the rings. Edge of an image can be represented as the dissimilarity in pixel neighborhood [4]. Therefore, linear decomposition of ICA can improve edge detection.…”

Section: Edge Detectionmentioning

confidence: 99%

Diagnosis of keratoconus using novel elevation mapping technique from smartphone image

Foysal¹,

Ankur²

2023

Multimodal Image Exploitation and Learning 2023

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Keratoconus is a progressive eye disease prevalent worldwide. Keratoconus is caused by the change in curvature of the eyeball. An unevenly shaped lens causes blurry and inaccurate vision in the patient, which eventually may cause blindness. The existing keratoconus detection techniques use a less accurate keratoscope and bulky, expensive topography imaging (OCT) device, which causes inconvenience in diagnosing this disease in a remote and scarce setup. In this paper, I propose a novel smartphone-based keratoconus diagnosis technique that uses a smartphone camera to acquire a 2D image of the cornea with Placido disc reflection, and process different image processing techniques including entropy-based edge detection, multiple circles detections, finally calculating 3D topography of the eye in an app. The existing inconvenient methods of keratoconus detection can be replaced by our proposed method, which is accurate, quick, reliable, and simple for usage by clinicians and patients in remote settings.

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“…In contrast to the MSE, a higher magnitude of PSNR indicates a better performance. Researchers such as Yahya et al [22], Fu [44], Chi and Gao [45], Mendhurwar et al [43], Ren, Li and Chen [46], Sert and Avci [47], and Tang et al [48] have used PSNR as one of the evaluation measures for their work. El Araby et al [49], Hu et al [50], and Sudhakara and Jenaki Meena [51] have used both MSE and PSNR.…”

Section: ) Continuity Evaluationmentioning

confidence: 99%

“…El Araby et al [49], Hu et al [50], and Sudhakara and Jenaki Meena [51] have used both MSE and PSNR. Mendhurwar et al [43] also have used signal-to-noise ratio (SNR) to evaluate their method. This measure is defined as: (dB).…”

Section: ) Continuity Evaluationmentioning

confidence: 99%

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Tariq

Hamzah

et al. 2021

IEEE Access

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A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.INDEX TERMS Digital image processing, edge detection algorithm, image segmentation, assessment, validation, quality measures, reviews.

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Edge-Detection in Noisy Images Using Independent Component Analysis

Cited by 7 publications

References 12 publications

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning

Diagnosis of keratoconus using novel elevation mapping technique from smartphone image

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

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