Dhawan A P, Arata L
Department of Electrical and Computer Engineering, University of Cincinnati, OH 45221.
Comput Methods Programs Biomed. 1993 Jul;40(3):203-15. doi: 10.1016/0169-2607(93)90058-s.
In image analysis applications, segmentation of gray-level images into meaningful regions is an important low-level processing step. Various approaches to segmentation investigated in the literature, in general, use either local information of gray-level values of pixels (region growing based methods, for example) or the global information (histogram thresholding based methods, for example). Application of these approaches for segmenting medical images often does not provide satisfactory results. Medical images are usually characterized by low local contrast and noisy or faded features causing unacceptable performance of local information based segmentation methods. In addition, because of a large amount of structural information found in medical images, global information based segmentation methods yield inadequate results in region extraction. We present a novel approach to image segmentation that combines local contrast as well as global gray-level distribution information. The presented method adaptively learns useful features and regions through the use of a normalized contrast function as a measure of local information and a competitive learning based method to update region segmentation incorporating global information about the gray-level distribution of the image. In this paper, we present the framework of such a self organizing feature map, and show the results on simulated as well as real medical images.
在图像分析应用中，将灰度图像分割成有意义的区域是一个重要的低级处理步骤。文献中研究的各种分割方法，一般来说，要么使用像素灰度值的局部信息（例如基于区域生长的方法），要么使用全局信息（例如基于直方图阈值化的方法）。将这些方法应用于医学图像分割时，往往不能提供令人满意的结果。医学图像通常具有局部对比度低、特征有噪声或模糊的特点，这导致基于局部信息的分割方法性能不佳。此外，由于医学图像中存在大量结构信息，基于全局信息的分割方法在区域提取方面产生的结果也不充分。我们提出了一种新颖的图像分割方法，该方法结合了局部对比度和全局灰度分布信息。所提出的方法通过使用归一化对比度函数作为局部信息的度量，并采用基于竞争学习的方法来更新区域分割，同时纳入关于图像灰度分布的全局信息，从而自适应地学习有用的特征和区域。在本文中，我们展示了这种自组织特征映射的框架，并展示了在模拟医学图像和真实医学图像上的结果。
