Comparative Analysis of Image Enhancement Techniques for Chest X-ray Images

Sharma, Megha; Kumar, Dharmendra

doi:10.1109/cises54857.2022.9844364

Cited by 3 publications

(3 citation statements)

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“…Megha et.al. [20] 29.62 CLAHE with DWT After analyzing the value in table 2 and with visual observation shown in figure 4, it is clearly seen the differences of performance of super resolution. To verify the effectiveness of the proposed method, PSNR and SSIM metrics are used for quality assessment.…”

Section: Resultsmentioning

confidence: 95%

“…[3] also proposed a study In which various techniques (HE, CLAHE, Gamma, TVF, AMF) are compared with different neural network, and gave advice that single method is not enough to get good result, so author focus on an advance pre-processing technique. M. Sharma et al [20] Proposed a study in which CLAHE method is used and compare with combination like morphological operation, median filter, and discrete cosine transform (DCT), discrete wavelet transform (DWT) and find that CLAHE + DWT is better than other and obtained 29.62 value of PSNR. Evaluation parameter Peak Signal to Noise Ratio (PSNR) is use to describe the quality assessment.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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

Sharda Rani

2023

IJRITCC

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“…Several approaches have been employed to enhance medical image quality, particularly in chest X-ray data. A histogram equalization-based model has shown good result in enhancing chest X-ray data, providing better visual results for interpretability and noise reduction [36,37,38]. The implementation of enhancement method for specific cases was also done such as to boost deep learning model for classification tasks in Covid-19 data using histogram equalization-based method [39,40,41] [44], the implementation of EFF was also carried out for the same cases by Setiawan [45].…”

Section: Related Workmentioning

confidence: 99%

Automatic Radiology Report Generator Using Transformer With Contrast-Based Image Enhancement

Tsaniya,

Fatichah,

Suciati

2024

IEEE Access

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Writing radiology reports based on radiographic images is a time-consuming task that demands the expertise of skilled radiologists. Consequently, the integration of technology capable of automated report generation would be advantageous. Developing a coherent predictive text is the main challenge in automatic report generation. It is necessary to develop methods that can increase the relevance of features in producing predictive text. This study constructed a medical report generator model using the transformer approach and image enhancement implementation. To leverage the visual and semantic features, an approach to enhance the noise-prone nature of the medical image is explored in this study along with the transformers method to generate a radiology report based on Chest X-ray images. Four contrast-based image enhancement methods were used to investigate the effect of image enhancement techniques on the radiology report generator. The encoder-decoder model is used with text feature embedding using Bidirectional Encoder Representation from Transformer (BERT) and visual feature extraction utilizing a pre-trained model ChexNet and Multi-Head Attention (MHA) mechanism. The performance of the MHA model with gamma correction is 5% in better with a 0.377 value using the Bilingual Assessment Understudy (BLEU) with 4 n-gram evaluation. MHA also produces 15% better results with a 0.412 value than the baseline model. This method is able to outperform the baseline model and other previous works. It can be concluded that the use of transformer MHA encoder layer and BERT is effective in leveraging visual and text features. Additionally, the inclusion of an image enhancement approach has been found to have a positive impact on the model's performance.

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