With the advent of the coherent age the implementation of massive digital signal processors (DSP) co-integrated with high speed AD and DA converters became feasible allowing for the realization of huge flexibility of transponders. Today the implementation of variable transponders is mainly based on variable programming of DSP to support different modulation formats and symbol rates. Modulation formats with high flexibility are required such as pragmatic QAM formats and hybrid modulation formats. Furthermore, we report on an implementable probabilistically shaping technique allowing for adjusting the bitrate. We introduce fundamental characteristics of all modes and describe basic operation principles. The modifications of the operational modes are enabled simply by switching between different formats and symbol rates in the DSP to adjust the transponders spectral efficiency, the bitrate and the maximum transmission distance. A fine granularity in bitrate and in maximum transmission distance can be implemented especially by hybrid formats and by probabilistically shaped formats. Furthermore, latter allow for ~+25% increase of the maximum transmission distance due their operation close to the Shannon limit as a consequence of their 2D Gaussian like signal nature. If the flexibility and programmability of transponders is implemented, it can be utilized to support different strategies for the application. The variability in symbol rate is mainly translated into variability in bitrate and in bandwidth consumption. Contrary the variable spectral efficiency translates into a variation of the maximum transmission reach and of the bitrate. A co-adjustment of both options will lead to a superior flexibility of optical transponders to address all requirements from application, transponder and fiber infrastructure perspective.
Multiple light scattering in tissue limits the penetration of optical coherence tomography (OCT) imaging. Here, we present in vivo OCT imaging of a live mouse using wavefront shaping (WS) to enhance the penetration depth. A digital micromirror device was used in a spectral-domain OCT system for complex WS of an incident beam which resulted in the optimal delivery of light energy into deep tissue. Ex vivo imaging of chicken breasts and mouse ear tissues showed enhancements in the strength of the image signals and the penetration depth, and in vivo imaging of the tail of a live mouse provided a multilayered structure inside the tissue.
Proc. SPIE. 9034, Medical Imaging 2014: Image Processing
KEYWORDS: Image processing algorithms and systems, Optical coherence tomography, Magnetic resonance imaging, Image segmentation, Image processing, 3D modeling, Medical imaging, Neuroimaging, 3D image processing, Brain
Segmentation of 3D medical structures in real-time is an important as well as intractable problem for clinical applications due to the high computation and memory cost. We propose a novel fast evolving active contour model in this paper to reduce the requirements of computation and memory. The basic idea is to evolve the brief represented dynamic contour interface as far as possible per iteration. Our method encodes zero level set via a single unordered list, and evolves the list recursively by adding activated adjacent neighbors to its end, resulting in active parts of the zero level set moves far enough per iteration along with list scanning. To guarantee the robustness of this process, a new approximation of curvature for integer valued level set is proposed as the internal force to penalize the list smoothness and restrain the list continual growth. Besides, list scanning times are also used as an upper hard constraint to control the list growing. Together with the internal force, efficient regional and constrained external forces, whose computations are only performed along the unordered list, are also provided to attract the list toward object boundaries. Specially, our model calculates regional force only in a narrowband outside the zero level set and can efficiently segment multiple regions simultaneously as well as handle the background with multiple components. Compared with state-of-the-art algorithms, our algorithm is one-order of magnitude faster with similar segmentation accuracy and can achieve real-time performance for the segmentation of 3D medical structures on a standard PC.
A novel noise reduction algorithm is proposed for reducing the noise and enhancing the contrast in 3D Optical
Coherence Tomography (OCT) images. First, the OCT image is divided into two subregions based on the local noise
property: the background area in which the additive noise is dominant and the foreground area in which the
multiplicative noise is dominant. In the background, the noise is eliminated by the 2D linear filtering combined with the
frame averaging. In the foreground, the noise is eliminated by the 3D linear filtering-an extension of the 2D linear
filtering. Therefore, the denoised image is reconstructed according to the combination of the denoised background and
foreground. The above procedure can be formulated with a bi-linear model which can be solved efficiently. The
proposed bi-linear model can dramatically improve image quality in 3D images with heavy noise and the corresponding
linear filter kernel in 2D can be performed in real time.
The filter kernel we used is introduced based on the linear noise model in OCT system. The noise model used in the filter
kernel includes both the multiplicative (speckle) noise and the additive (incoherent) noise, where the latter is not
considered in the most existing linear speckle filters and wavelet filters. Also, the filter kernel can be treated as a low
pass filter and can be applied to frequency extraction. Therefore an image contrast enhancement method is introduced in
the frequency domain based on the frequency decomposing and weighted combination. A set of experiments are carried
out to verify the effectiveness and efficiency of the proposed algorithm.
In this paper, we propose a zerotree wavelet image/video coding technique with resilience to transmission errors which typically occur on noisy channels. A key tool that we employ is the bit partitioning algorithm and the bit reorganization algorithm which is called the EREC (Error Resilient Entropy Code). In order to take full advantage of the bit reorganization algorithm, the bit partitioning algorithm composes the data as separate code-blocks although the zerotree wavelet coding algorithm is not block-based compression technique. The bit reorganization algorithm requires a very low redundancy for the sequential transmission of variable length blocks that offer virtually guaranteed code and block synchronization. We present simulation results verifying the error resiliency of the proposed algorithm both for image coding which uses the wavelet transform and for video coding which uses the 3D wavelet transform. Experimental results show that the proposed coders outperform the existing error resilient coders for both noise-free channels and noisy channels. In addition, we confirm that the proposed algorithm is more error-resilient than the previously reported error-resilient coders for various channel error conditions.