IEEE Trans Image Process. 2014 May;23(5):2033-46. doi: 10.1109/TIP.2014.2307475.
We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on nonnegative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.
我们提出了一种新的用于无监督图像分割的数学和算法框架，这是各种图像处理应用中的关键步骤。我们发现，大多数现有的分割方法在组织病理学图像上并不成功，这促使我们研究更广泛类别的图像分割，即那些没有要分割的区域之间明显边界的图像。我们将这些图像建模为随机图像的遮挡，我们称之为纹理，并表明局部直方图是分割它们的有用工具。基于我们的理论结果，我们描述了一个灵活的分割框架，该框架借鉴了非负矩阵分解和图像反卷积的现有工作。在合成纹理镶嵌和真实组织学图像上的结果表明了该方法的前景。
