An Analysis of Image Enhancement Techniques for Dental X-ray Image Interpretation

Ahmad, Siti Arpah; Taib, Mohd Nasir; Khalid, Norlin; Taib, Haslina

doi:10.7763/ijmlc.2012.v2.133

Cited by 40 publications

(8 citation statements)

References 17 publications

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“…The AHE enhances the image contrast and enlarges the noise. In some cases, enhancement process results in image distortion in some detail area, which affects the visual diagnosis of clinicians for the enhanced image [ 20 , 21 ]. We need to not only enhance the features and image contrast, but also restrict the magnified noise.…”

Section: Methodsmentioning

confidence: 99%

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

Yang

et al. 2013

Computational and Mathematical Methods in Medicine

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A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

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Section: Methodsmentioning

confidence: 99%

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

Yang

et al. 2013

Computational and Mathematical Methods in Medicine

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“…Contrast Limited Adaptive Histogram Equalization (CLAHE) locally processes all the small regions of the image, where the contrast is enhanced through adaptive HE, and the chances of noise amplification can be reduced as well. Ahmed et al [16] proposed an image enhancement algorithm for dental X-ray images based on the adaptive HE technique. Gamma correction can work as a non-linear contrast enhancement technique applied to each pixel and independently modifies the dynamic range of the image.…”

Section: Cxr Contrast Enhancementmentioning

confidence: 99%

Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images

et al. 2020

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Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is important in computer-aided diagnosis. In this paper, we propose an adaptive pre-processing approach for segmenting the lung regions from CXR images using convolutional neural networks-based (CNN-based) architectures. It is comprised of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Second, adaptive image binarization is applied to CXR images to separate the image foreground and background. Third, CNN-based architectures are trained on the binarized images for image segmentation. The experimental results show that the proposed pre-processing approach is applicable and effective to various CNN-based architectures and can achieve comparable segmentation accuracy to that of state-of-the-art methods while greatly expediting the model training by up to 20.74 % and reducing storage space for CRX image datasets by down to 94.6 % on average.

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“…Fig. 2 depicts the preprocessing steps for the CXR image dataset, which includes resizing, normalization, and CLAHE [33] applied in sequence.…”

Section: Preprocessingmentioning

confidence: 99%

COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images

Rajpal¹,

Agarwal²,

Rajpal³

et al. 2020

Preprint

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Coronaviruses constitute a family of virus that gives rise to respiratory diseases. Coronavirus disease 2019 is an infectious disease caused by a newly discovered coronavirus also termed as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its rapid spread, WHO has declared COVID-19 outbreak a pandemic on 11th March 2020. Reverse transcription-polymerase chain reaction (RT-PCR) test is popularly used worldwide for the detection of COVID-19. However, due to the high false-negative rate of RT-PCR test, chest X-ray (CXR) imaging is emerging as a feasible alternative for the detection of COVID-19. In this work, we propose a multiclass classification model COV-ELM, based on the extreme learning machine which classifies the CXR images into one of the three classes, namely COVID-19, normal, and pneumonia. The choice of ELM in this work has been motivated by its significantly short training time as compared to conventional gradient-based learning algorithms. After some preprocessing, we extract a pool of features based on texture and frequency. This pool of features serves as an input to the ELM and a 10-fold cross-validation method is employed to evaluate the proposed model. For experimentation, we use chest X-ray (CXR) images from three publicly available sources. The results of applying COV-ELM on test data are quite promising. The COV-ELM achieved a macro average F1-score of 0.95 and the overall sensitivity of 0.94 ± 0.02 at 95% confidence interval. When compared to state-of-the-art machine learning algorithms, the COV-ELM is found to outperform in a three-class classification scenario. The main advantage of COV-ELM is that its training time being quite low, as bigger and diverse datasets become available, it can be quickly retrained as compared to its gradient-based competitor models.

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An Analysis of Image Enhancement Techniques for Dental X-ray Image Interpretation

Cited by 40 publications

References 17 publications

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images

COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images

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