Application of Histogram Equalization for Image Enhancement in Corrosion Areas

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

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“…The process of HE is as follows [ 32 ]: Calculate the pixel number in each grey level of the real input image. The

th value represents the grey level that is given by

.…”

Section: Methodsmentioning

confidence: 99%

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

et al. 2022

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“…A rough surface ends up with huge speckle patches that in turn reduce the variance, within the fixed evaluation length. The histogram mapping of an illuminated area of interest (ROI) from machined surface photographs was studied to see if there was any fluctuation in histogram frequency, which helps with surface roughness evaluation [28].…”

Section: Methodsmentioning

confidence: 99%

Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

Kumar¹,

Thenmozhi²,

Bommi

et al. 2022

Journal of Nanomaterials

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Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.

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“…The basic idea is that the original nonuniform probability distribution gray map of the CT image is nonlinearly stretched by the histogram and transformed into a uniform probability distribution map [ 16 ]. In other words, the image clarity is enhanced by adjusting the size of the gray value [ 17 ]. The theoretical formula of histogram equalization is as follows.…”

Section: Methodsmentioning

confidence: 99%

A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm

Hou

Jia

Hou

et al. 2021

Computational and Mathematical Methods in Medicine

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The coronavirus disease 2019 (COVID-19) is a substantial threat to people’s lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K -means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.

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Application of Histogram Equalization for Image Enhancement in Corrosion Areas

Cited by 24 publications

References 17 publications

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization

A Study on Weak Edge Detection of COVID-19’s CT Images Based on Histogram Equalization and Improved Canny Algorithm

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