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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.
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