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Abstract
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Biomedical image processing plays a critical role in modern healthcare, as it enables accurate visualization, analysis, and interpretation of medical data obtained from imaging modalities such as MRI, CT, X-ray, and ultrasound. The quality of image processing directly impacts diagnosis, treatment planning, and monitoring of diseases. However, biomedical images are often affected by noise, artifacts, low contrast, and large data sizes, which make efficient and precise processing techniques essential.
The necessity of this research arises from the growing demand for advanced algorithms that can enhance image quality while preserving important diagnostic details. Conventional image processing methods often face challenges in balancing compression, denoising, and edge preservation, which are crucial in medical applications. Wavelet-based techniques have proven to be powerful tools in this domain, as they offer multi-resolution analysis and efficient representation of image features.
Biorthogonal wavelets, in particular, provide distinct advantages over traditional orthogonal wavelets by allowing symmetric filters, linear phase properties, and improved reconstruction accuracy. These properties make them especially suitable for biomedical images, where symmetry and preservation of fine details are vital for clinical interpretation. Developing a biorthogonal wavelet algorithm for biomedical image processing ensures not only effective noise reduction and image compression but also the preservation of anatomical structures, which is critical for reliable diagnosis.
The importance of this research lies in its potential to advance the state of biomedical imaging by introducing a computationally efficient, robust, and clinically applicable algorithm. Such advancements can improve diagnostic accuracy, reduce storage and transmission requirements for medical data, and support telemedicine and AI-assisted healthcare systems. Furthermore, the outcomes of this research can be extend
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