Mr. Alex Liew Profile

Alex Liew

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Publications (3)

Proceedings Article | 15 June 2023 Poster + Paper

Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features

Alex Liew, Sos Agaian, Liang Zhao

Proceedings Volume 12526, 125260O (2023) https://doi.org/10.1117/12.2664239

KEYWORDS: RGB color model, Skin cancer, Education and training, Binary data, Visual process modeling, Medical statistics, Histograms, Feature extraction, Skin, Image classification

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Proceedings Article | 15 June 2023 Poster + Paper

Edge preserving noise removal via extended weighted Okada bitonic plane kernels for spectral domain optical coherence tomography

Alex Liew, Sos Agaian

Proceedings Volume 12526, 125260T (2023) https://doi.org/10.1117/12.2661591

KEYWORDS: Tunable filters, Optical coherence tomography, Image filtering, Digital filtering, Speckle, Denoising, Signal filtering, Eye, Gaussian filters, Wavelets

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Spectral Domain Optical Coherence Tomography (SD-OCT) is a widely used imaging technique in ophthalmology. However, it often suffers from severe distortion due to speckle noise, which can obscure critical retinal structures and lesions. These distortions can significantly reduce accuracy of image-based diagnostic tasks. Developing effective techniques for reducing speckle noise and improving the quality of SD-OCT images is crucial. However, there are two main challenges in removing speckle noise: (1) balancing the removal of noise while preserving essential image details, and (2) that speckle noise can have varying intensity and size levels, making it challenging to develop a onesize-fits-all approach. If too much noise is removed, the image may become overly smoothed and lose essential details. On the other hand, if noises are not sufficiently removed, the image may still appear noisy and distorted. Different methods and algorithms may need to be used depending on the noise characteristics and the specific image being processed. Despite these challenges, various denoising techniques, such as wavelet-based, non-local means, and adaptive median filtering, have been proposed in the literature. Each method has its strengths and weaknesses, and the choice of the method should be based on the noise characteristics and the desired trade-off between noise removal and image preservation. While recent works in deep learning have shown promise in denoising OCT images, they require extensive training data and complex hardware, limiting their practicality in many settings. This paper presents an edge-preserving noise removal method for improving the quality of SD-OCT by reducing the effect of noise using a new morphology-based bitonic filter. This filter is created by combining extended Okada with various kernel sizes. This approach allows us to efficiently remove speckle noise from OCT images while minimizing the loss of details and enhancing image quality. Compared to existing methods, the presented approach is more efficient and requires fewer computational resources. It could enhance the accuracy of image-based diagnostic tasks, ultimately benefiting patients and clinicians alike.

Proceedings Article | 27 May 2022 Poster + Paper

Alpha mean trim texture descriptors for optical coherence tomography eye classification

Alex Liew, Larry Ryan, Sos Agaian

Proceedings Volume 12100, 121000F (2022) https://doi.org/10.1117/12.2618059

KEYWORDS: Speckle, Eye, Image classification, Visualization, Speckle pattern, Image filtering, Optical coherence tomography

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