Contrast Enhancement and Noise Reduction of chest X-ray images

Sharma, Megha; Kumar, Dharmendra

doi:10.1109/cises54857.2022.9844388

Cited by 2 publications

(2 citation statements)

References 9 publications

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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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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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“…This method modifies the image's contrast for enhancing image quality [3]. Sharma and Kumar [4] suggested improving the contrast of chest X-ray images by combining HE and CLAHE techniques. At the same time, noise reduction such as median filter and discrete cosine transform are also used to reduce the noise produced by CLAHE in order to produce high-contrast color images.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Contrast Enhancement Techniques Using Rule-Based System and Intensification Operator

Suzelawati Zenian,

Tan Jia Xuan,

Siti Aisyah Tumiran

2024

ARASET

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Contrast enhancement is a crucial step in digital image processing to ensure the production of high-quality images. This paper presents two distinct fuzzy techniques designed to address the nuanced concept of contrast. that utilize rule-based system and intensification operator, respectively, to handle the inherent vagueness associated with the concept of contrast. The fuzzy rule-based system calculates the intensity value of the output image based on the contrast measure (standard deviation) of the input image, thereby enabling effective contrast enhancement for low contrast images. Meanwhile, the fuzzy intensification operator aims to accentuate the differences in intensity levels within the image, thereby enhancing the contrast between various features and details. The experimental results show better image contrast is obtained using both methods. However, fuzzy rule-based approach shows more images with better quality compared to the intensification operator approach. The performance of these techniques is evaluated both visually and quantitatively.

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Contrast Enhancement and Noise Reduction of chest X-ray images

Cited by 2 publications

References 9 publications

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

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

Fuzzy Contrast Enhancement Techniques Using Rule-Based System and Intensification Operator

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