Dr. Ling Li Profile

Dr. Ling Li

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Proceedings Article | 1 April 2024 Poster + Paper

Despecking of ultrasound image using LENet-based nonlocal-means method

Houqiang Yu, Ling Li, Min Zheng, Wu Qiu, Mingyue Ding

Proceedings Volume 12932, 1293218 (2024) https://doi.org/10.1117/12.3006341

KEYWORDS: Tunable filters, Speckle, Image filtering, Ultrasonography, Image processing, Feature extraction, Windows, Denoising, Digital filtering, Visualization

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Speckle noise is an integral component in medical ultrasound imaging, which have a random granular pattern formation. This noise degrades the visual quality of ultrasound images and complicates image-based interpretation and diagnosis. The removal of interference-induced noise is a primary challenge as ultrasound image studies seek to achieve higher accuracy and characterize more subtle small and low-contrast lesions. In this study, a novel method, which combines the nonlocal-means (NLM) filter with a simple unsupervised deep model named Laplacian Eigenmaps network (LENet), has been proposed for ultrasonic speckle reduction. The proposed method exploits both the global features, redundancy information and self-similarity properties of noisy images, which first extract features from the noisy image by the Laplacian Eigenmaps algorithm, and then apply it to refine the image self-similarities weight for helping the NLM filter to provide better despeckling performance. Specifically, this is a two-stage approach that the first stage is to learn filter banks from a small quantity of training samples by LENet network and the following stage is to utilize the output eigenvectors as similarity metrics of pixels within the NLM filter. The performance of our approach is compared with related state-of-the-art methods on synthetic images, simulated image and real ultrasound images. The results show that our method can provide better noise removal ability over many previously despecking filters.

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