Image enhancement on digital x-ray images using N-CLAHE

Koonsanit, Kitti; Thongvigitmanee, Saowapak S.; Pongnapang, Napapong; Thajchayapong, P.

doi:10.1109/bmeicon.2017.8229130

Cited by 81 publications

(33 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the machine learning classifiers, use a pixel array as a data source, any systematic difference in pixel intensity between the datasets would introduce sampling bias in the To minimize the effect of sampling bias, we applied histogram equalization to images using the N-CLAHE method described by [70]. The method both normalizes images and enhances small details, textures and local contrast by first globally normalizing the image histogram followed by application of Contrast Limited Adaptive Histogram Equalization (CLAHE) [71].…”

Section: B Data Samplingmentioning

confidence: 99%

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

et al. 2020

View full text Add to dashboard Cite

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

show abstract

Section: B Data Samplingmentioning

confidence: 99%

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Where I γ is the output image for gamma correction and γ is the gamma correction factor. Also, we apply the contrast limited adaptive histogram equalization (CLAHE) to enhance the image Local Contrast [16]. To calculate the clip limit for the CLAHE algorithm, we used Equation 2.…”

Section: Data Augmentationmentioning

confidence: 99%

Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

Hassan¹,

Abdel-Nasser²,

Saleh³

et al. 2021

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

show abstract

“…The Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details, textures and local contrast of the images (Zuiderveld, 1994). Local details can therefore be enhanced even in the regions that are darker or lighter than most of the image (Koonsanit et al, 2017). To avoid over-fitting, since the number of CT volumes is limited, we applied data augmentation strategies such as random transformations.…”

Section: Preprocessing and Data Augmentationmentioning

confidence: 99%

A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans

El-Bana

Al-Kabbany

Sharkas

2020

PeerJ Computer Science

View full text Add to dashboard Cite

We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and Computed Tomography (CT) scans, and the classification and segmentation of related infection manifestations. Even though it is arguably not an established diagnostic tool, using machine learning-based analysis of COVID-19 medical scans has shown the potential to provide a preliminary digital second opinion. This can help in managing the current pandemic, and thus has been attracting significant research attention. In this research, we propose a multi-task pipeline that takes advantage of the growing advances in deep neural network models. In the first stage, we fine-tuned an Inception-v3 deep model for COVID-19 recognition using multi-modal learning, that is, using X-ray and CT scans. In addition to outperforming other deep models on the same task in the recent literature, with an attained accuracy of 99.4%, we also present comparative analysis for multi-modal learning against learning from X-ray scans alone. The second and the third stages of the proposed pipeline complement one another in dealing with different types of infection manifestations. The former features a convolutional neural network architecture for recognizing three types of manifestations, while the latter transfers learning from another knowledge domain, namely, pulmonary nodule segmentation in CT scans, to produce binary masks for segmenting the regions corresponding to these manifestations. Our proposed pipeline also features specialized streams in which multiple deep models are trained separately to segment specific types of infection manifestations, and we show the significant impact that this framework has on various performance metrics. We evaluate the proposed models on widely adopted datasets, and we demonstrate an increase of approximately 2.5% and 4.5% for dice coefficient and mean intersection-over-union (mIoU), respectively, while achieving 60% reduction in computational time, compared to the recent literature.

show abstract

Image enhancement on digital x-ray images using N-CLAHE

Cited by 81 publications

References 6 publications

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans

Contact Info

Product

Resources

About