We are using Optical Coherence Tomography (OCT) to image structure and function of the developing embryonic heart in avian models. OCT allows one to non-invasively image living hearts with microscopic resolution, and to visually and quantitatively analyze development. Due to the diminutive size and rapid movements of the early embryonic heart, OCT imaging provides a unique ability to study anatomy and function. We believe that OCT has the requisite spatial and temporal resolution and is hence an important tool to facilitate understanding of the underlying mechanisms responsible for normal/abnormal heart development [1
]. However, noise present in OCT imaging systems [2
] limits our ability to interpret, visualize and analyze image data which is crucial to the understanding of early cardiac development. The purpose of our study is to address this limitation by creating an algorithm for noise reduction in OCT images and evaluating its performance both visually and quantitatively through volumetric visualization and image segmentation. The novelty of our noise reduction technique lies in its ability to optimally reduce noise based on the characteristics of a particular image data set.
Due to its deleterious effects on coherent imaging systems such as ultrasound and OCT, there has been significant effort to characterize and reduce noise [2
]. The two most common noise sources are shot noise, which is additive in nature and can be adequately described by the Additive White Gaussian Noise (AWGN) process, and speckle noise, which is multiplicative in nature and harder to eliminate due to its signal dependency. In fact, speckle carries useful information about the underlying tissue structure [11
]. OCT is very similar to ultrasound and a brief review of shot and speckle noise reduction in ultrasound is in order. Shot noise reduction is applied both during acquisition [17
] and post-acquisition using simple image processing techniques [22
] such as an averaging filters, median filters and Gaussian low-pass filters. However, many of these filtering techniques tend to remove useful features from images. One of the most effective technique for shot noise removal is the phase preserving non-orthogonal wavelet (NW) filtering technique proposed by Kovesi [16
]. As for speckle noise removal from ultrasound images, spatial domain techniques have been employed including the one proposed by Xie et al. [19
] who applied a salient boundary enhancement technique with a speckle suspension term, and Dutt and Greenleaf [13
], who employed a local statistical model to quantify the extent of speckle formation and subsequently used an unsharp masking filter to suppress speckle. As for transform domain techniques, wavelet-based speckle suppression has been reported [12
]. More recently, Fan et al. [25
] combined pyramid decomposition of images with anisotropic diffusion filtering to reduce speckle in ultrasound images of phantoms and liver. For OCT images, shot noise has been reduced using post-acquisition image processing techniques [22
] such as averaging filters, median filters and Gaussian filters. There are also reports on speckle reduction techniques in OCT including physical techniques [2
], those applied prior to image formation [6
], and post-acquisition, image processing techniques such as hybrid median filter (HMF), Wiener filter, ELEE filter, symmetric nearest neighbor (SNN) filter, Kuwahara filter, adaptive Wiener filter, rotating kernel transformation (RKT), anisotropic diffusion filtering, orthogonal and non-orthogonal wavelet filters [3
]. Ozcan et. al [7
] have compared the relative performances of the ELEE filter, two wavelet transform based filters, the HMF, SNN, a Kuwahara filter, and the adaptive Wiener filter, and have argued that post-acquisition digital image processing is advantageous because it does not require the additional acquisition of compounding angles required by the physical technique for speckle reduction. Puvanathasan and Bizheva [9
] have used a fuzzy thresholding algorithm in the wavelet domain for speckle reduction in OCT images of a human finger tip, and have compared their technique with the Wiener and Lee filters.
In this paper, we create an algorithm to reduce both shot and speckle noise through digital image processing. The Kovesi NW filtering technique, originally applied to video surveillance data, can greatly reduce shot noise. However, manual optimization of parameters can be a daunting and unsatisfying task. Hence, we will investigate methods for automatically optimizing the wavelet filter bank for OCT. We call our technique Optimized Non-orthogonal Wavelet (ONW) denoising. To reduce speckle, we use an enhanced version of the Laplacian Pyramid Nonlinear Diffusion (LPND) technique used by Fan et. al on ultrasound images of the liver and carotid artery [25
]. Since speckle size depends on imaging parameters such as the characteristics of the light source, the spot size, and sampling rate, we have investigated adaptive optimization of LPND parameters, and call the method Adaptive LPND (ALPND).
We have identified three approaches for evaluation of noise reduction. First, there are quantitative measures on individual images such as edge preservation (β) [15
], structural similarity measure (SSIM) [26
], and contrast-to-noise ratio (CNR) [27
], as reported in a recent work by Fan et al [25
]. We will create a weighted sum of these measures and use this scalar image quality criterion to optimize ALPND. This measure will also be used to evaluate other noise reduction algorithms. Second, as described by Frangakis et al [28
], one can evaluate the effect of noise reduction on 3D image visualization. We will investigate how noise reduction affects both isosurface, surface rendering and direct volume rendering [29
]. Gradients provide enhanced volume visualization of internal surfaces and tissue boundaries [30
], and we are particularly interested in the role of noise reduction in improving visualization through accurate estimation of gradients in data. Third, one can determine the effect of noise reduction on segmentation [33
]. We used a simple tolerance based seeded region growing algorithm available within the visualization package Amira [34
] and a more sophisticated semi-automatic image contour segmentation tool called LiveWire [35
] to both qualitatively and quantitatively evaluate the effect of noise reduction.
The rest of the paper is organized as follows. In Section 2, we briefly describe baseline denoising algorithms such as median filtering, Wiener filtering, and orthogonal wavelet (OW) filtering, followed by our proposed ONW-ALPND denoising algorithm. In Section 3, we discuss methods for evaluating the performance of image denoising algorithms. Section 4 presents results of our proposed denoising algorithm along with quantitative/qualitative comparisons to baseline algorithms. This is followed by a discussion in Section 5.