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Journal International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE) Abstract

Ultrasound tomography is a powerful and widely utilized imaging technique in the field of medical diagnostics. Its non-invasive nature and high sensitivity in detecting small objects make it an invaluable tool for healthcare professionals. However, a significant challenge associated with ultrasound tomography is that the reconstructed images often contain noise. This noise can severely compromise the accuracy and interpretability of the diagnostic information derived from these images. In this paper, we propose and rigorously evaluate the application of a median filter to address and mitigate noise artifacts in the reconstructed images obtained through the distorted born iterative method (DBIM). The primary aim is to enhance the quality of these images and thereby improve diagnostic reliability. The effectiveness of our proposed noise reduction approach is quantitatively assessed using the normalized error evaluation metric, which provides a precise measure of improvement in image quality. Furthermore, to enhance the interpretability and utility of the reconstructed images, we incorporate a basic machine learning technique known as K-means clustering. This method is employed to automatically segment the reconstructed images into distinct regions that represent objects, background, and noise. Hence, it facilitates a clearer delineation of different components within the images. Our results demonstrate that K-means clustering, when applied to images processed with the proposed median filter method, effectively delineates these regions with a significant reduction of noise. This combination not only enhances image clarity but also ensures that critical diagnostic details are preserved and more easily interpreted by medical professionals. The substantial reduction in noise achieved through our approach underscores its potential for improving the accuracy and reliability of ultrasound tomography in medical diagnostics.

Conclusion

The study demonstrates that integrating a median filter into the DBIM iterative process significantly reduces noise in reconstructed ultrasound tomographic images.The application of K-means clustering further enhances image interpretability by automatically segmenting regions into object, background, and noise.These combined methodologies improve the visual fidelity of reconstructions and streamline diagnostic interpretation, offering a compact and explainable enhancement pipeline suitable for clinical and low-cost diagnostic settings.

Future Research

Future research should focus on validating the proposed method on real clinical ultrasound data to assess its real-world applicability. Exploring adaptive filtering techniques, such as anisotropic diffusion or guided filters, that can adjust based on local image characteristics could further improve performance. Additionally, integrating region-growing or active contour methods into the segmentation step may enhance the handling of irregularly shaped lesions. Finally, hybrid frameworks combining the current physics-based inversion with learning-based priors or data-driven regularizers could yield further improvements in reconstruction quality, particularly in challenging imaging scenarios. These advancements will contribute to more accurate and reliable ultrasound tomography for improved medical diagnostics and potentially expand its applications to other areas of biomedical imaging.

References

1. Digital MammographyRadiology. digital contents abstract figures references information authors metrics... doi.org/10.1148/radiol.2342030897Digital MammographyRadiology digital contents abstract figures references information authors metrics doi 10 1148 radiol 2342030897

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