An Adaptive Enhancement of X-Ray Images

Georgieva, Veska; Kountchev, Roumen; Draganov, Ivo

doi:10.1007/978-3-319-00029-9_7

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…The histogram specification method has been utilized by the researcher in the past to boost the contrast in the image, but it has certain limitations and complications as well because it illuminates each pixel in the image and makes some parts of the image's more sparkling regions brighter [27,28]. Similarly, another method of histogram like adaptive histogram equalization may be used; however, the drawback of this technique is that it not only improves the picture but it also improves noise in the picture [17]. Here, in the proposed hybrid framework, after the fusion process, the Gaussian filter is applied to the fused image to eradicate the Gaussian commotion, and finally, the Wiener filter is used to reduce blurriness and improve the quality of the picture as well.…”

Section: Discussionmentioning

confidence: 99%

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A Novel Hybrid Framework to Enhance Dual‐Energy X‐Ray Images

et al. 2023

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This research paper presents an efficient hybrid framework for the enhancement of X-ray images using the DWT (discrete wavelet transform), Gaussian, and Wiener filters. Digital X-ray images perform an important role in detecting explosives or illegal items. However, lately used X-ray images have certain shortcomings like noise, blurriness, and low contrast. Noise affects the object’s edges and values of intensity of pixels which gives rise to ambiguity for the system to separate objects and also hampers the decision-making process. To overcome these issues, a new hybrid framework is proposed here that consists of two layers. First, it reads high- and low-energy X-ray images and fuses them through various fusion rules in the wavelet realm using DWT and is reconstructed using iDWT (inverse discrete wavelet transform). Subsequently, the Gaussian filter is applied to the fused image to remove the Gaussian noise, and finally, the Wiener filter is used to reduce blurriness and improve image quality. The quality of the image is measured using PSNR (peak signal-to-noise ratio) and MSE (mean squared error) values, which exhibits that an average of the MSE values of fused images is 40% lower, and the PSNR values are 10% higher than the single images. Experimental results show that this hybrid framework successfully reduced noise and blurriness and enhanced contrast and accuracy.

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Section: Discussionmentioning

confidence: 99%

“…The wavelet transform is said to perform a number of functions. WAVELETs have been found as one of the most popular terms for image processing [17]. The entire image is first converted using WAVELET transformation, and then, the encoding is performed to the entire set of WAVELET coefficients.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

A Novel Hybrid Framework to Enhance Dual‐Energy X‐Ray Images

et al. 2023

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“…Georgieva et al [12] analyzed image enhancement which is based on CLAHE following by morphological processing and noise reduction, based on wavelet packet decomposition and adaptive threshold of wavelet coefficient in high frequency subbands of the shrinkage decomposition. Visual quality improved, PSNR=29.815, enhanced X-ray images can be used for better visualization in 3D reconstitution.…”

Section: Cmentioning

confidence: 99%

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

Sharda Rani

2023

IJRITCC

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To maximize the accuracy of classification for medical images especially in chest- X ray, we need to improve quality of CXR images or high resolute images will be needed. Pneumonia is a lung infection caused by organism like bacteria or virus. Mostly Chest X-Ray (CXR) is used to detect the infection, but due to limitation of existing equipment, bandwidth, storage space we obtain low quality images. Spatial resolution of medical images is reduced due to image acquisition time, low radiation dose. Quality in medical images plays a major role for clinical diagnosis of disease in deep learning. There is no doubt that noise, low resolution and annotations in chest images are major constraint to the performance of deep learning. Researchers used famous image enhancement algorithms: Histogram equalization (HE), Contrast-limited Adaptive Histogram Equalization (CLAHE), De-noising, Discrete Wavelet Transform (DWT), Gamma Correction (GC), but it is still a challenging task to improve features in images. Computer vision and Super resolution are growing fields of deep learning. Super resolution is also feasible for mono chromatic medical images, which improve the region of interest. Multiple low-resolution images mix with high resolution and then reconstruct a target input image to high quality image by using Super Convolution Neural Network (SRCNN). The objective evaluation based on pixel difference-based PSNR and Human visual system SSIM metric are used for quality measurement. In this study we achieve effective value of PSNR (40 to 43 dB) by considering 30 images of different category (normal, viral or bacterial pneumonia) and SSIM value varies from 97% to 98%. The experiment shows that image quality of CXR is increased by SRCNN, and then high qualitative images will be used for further classification, so that significant parameter of accuracy will be finding in diagnosis of disease in deep learning.

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“…Hence, to improve the visual quality of the radiography images considering contrast and feature enhancement techniques like Linear Contrast, HE (Histogram Equalization), CLAHE (Contrast limited adaptive histogram equalization) and BPHE (Brightness Preserving Histogram Equalization) were developed. Georgieva et al [17] proposed a method that used CLAHE followed by morphological processing and noise reduction for x-ray image enhancement. The advantage of using CLAHE is that it improves the contrast of the image and reduces the noise in homogeneous areas.…”

Section: Related Workmentioning

confidence: 99%

Deep neural models for automated multi-task diagnostic scan management—quality enhancement, view classification and report generation

Karthik¹,

S²

2021

Biomed. Phys. Eng. Express

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The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval , automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest x-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the x-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications.

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An Adaptive Enhancement of X-Ray Images

Cited by 10 publications

References 5 publications

A Novel Hybrid Framework to Enhance Dual‐Energy X‐Ray Images

A Novel Hybrid Framework to Enhance Dual‐Energy X‐Ray Images

Investigation of Different Pre-processing Quality Enhancement Techniques on X-ray Images

Deep neural models for automated multi-task diagnostic scan management—quality enhancement, view classification and report generation

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