Model-based image segmentation

图像去噪和图像分割是许多图像处理和计算机视觉应用中的重要组成部分。去噪的目的是在保持区域边界(即边缘)的同时平滑图像的同质区域。同时，分割技术的目的是提取同质区域的边界。在传统的流水线去噪/分割方案中，由于去噪步骤或由于过量的噪声，分割步骤可能遭受重要信息的损失。与传统方法相比，将这两个步骤结合起来应该可以保留大部分丢失的信息。 在这项研究中，我们计划在不同的粒度上以迭代的方式结合斑点图像去噪和图像分割过程，而不是同时进行。这样，去噪和分割子结果将被两种方案在所有级别上利用。这应该有助于在增强和成功分割所需对象的同时平滑图像中不相关的细节。此外，我们计划将先验知识和光流信息融入到图像分割/去噪过程中。该方向应该提高所产生的分割结果的准确性，特别是当感兴趣的对象具有弱边缘时。我们还计划通过自适应地计算扩散系数来改进去噪扩散过程。这一步骤将以适度的复杂性改善去噪结果。 将该方法应用于实际的超声心动图和颈动脉超声图像。由于高频声波的零星性质，这些图像往往会受到斑点噪声和其他异常伪影的困扰。因此，它们为验证和测试我们提出的研究提供了一个完美的环境。我们建议的方案将有助于提高各种临床测量的可靠性，如左心室射血分数或颈动脉狭窄评估。所提出的分割方案也将在含有模拟斑点噪声的合成图像上进行验证和测试。

Image de-noising and image segmentation represent a crucial part in many image processing and computer vision applications. The goal of de-noising is to smooth homogenous regions of an image while preserving the region boundaries (i.e., edges). Meanwhile, segmentation techniques aim to extract the boundaries of homogenous regions. In traditional pipelined de-noising/segmentation schemes, the segmentation step may suffer from a loss of vital information due to the de-noising step, or due to the excess of noise. Combining these two steps should preserve much of this lost information compared to the traditional methods. In this research, we plan to combine speckle image de-noising and image segmentation processes in an iterative fashion at various levels of granularity, rather than simultaneously. This way, de-noising and segmentation sub-results would be utilized by both schemes at all levels. This should facilitate the ability to smooth irrelevant details from the image while enhancing and successfully segmenting the desired object. In addition, we plan to incorporate prior knowledge and optical flow information to the image segmentation/de-noising process. This direction should improve the accuracy of the produced segmentation results, especially when the object of interest has weak edges. We will also plan to improve the de-noising diffusion process by adaptively calculating diffusion coefficients. This step would improve de-noising results at a moderate complexity. The proposed combined segmentation-diffusion approach will be applied on real echocardiographic and carotid ultrasound images. These images tend to be plagued by speckle noise and other anomalous artifacts due to the sporadic nature of high frequency sound waves. Hence, they present a perfect environment for validating and testing our proposed research. Our proposed schemes would help improve the reliability of various clinical measures such as a left ventricular ejection fraction or a carotid artery stenosis assessment. The proposed segmentation schemes will be also validated and tested on synthetic images with simulated speckle noise.