Abstract
Medical images are useful in the study and diagnosis of a wide range of medical problems. The human body can be imaged using several different imaging techniques, such as X-ray, CT, MRI, and ultrasound. The significant challenge in the process of successful disease identification and diagnosis is separating the required part of the person from the background of medical images and it is achieved by the Hounsfield unit. The Hounsfield unit (HU) is a measurement of relative radio density. DICOM (Digital Imaging and Communications in Medicine) is a standard for the storage and transmission of medical images. The pixel data read from the DICOM file is the raw attenuation coefficient present in the file which is usually used for the visualization purpose. This study aims to rescale raw pixel values to the Hounsfield unit. To achieve this, the abdominal CT scan image of the CHOAS dataset is used. Each raw pixel of the DICOM image is rescaled to the Hounsfield unit using rescale intercept and rescale slope values. The obtained results are analyzed for the same Hounsfield unit range before and after Hounsfield scaling. By converting the raw pixel values into Hounsfield units, CT images can be displayed and analyzed with greater clinical significance.
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Acknowledgements
Authors are thankful to reviewers valuable comments and suggestions which improved the quality of the manuscript. The authors are grateful to Dr. Siddaroodha Sajjan, Associate Professor, Department of Radiology, BLDE(DU) Shri B M Patil Medical college, Vijayapura for his valuable suggestions and support in validating the experimental results.
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Bhusnurmath, R.A., Betageri, S. (2024). Enhancing CT Image Visualization and Analysis Through Rescaling Raw Pixel Values to Hounsfield Units. In: Santosh, K., et al. Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2023. Communications in Computer and Information Science, vol 2027. Springer, Cham. https://doi.org/10.1007/978-3-031-53085-2_2
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