Liu Xin, Langer Deanna L, Haider Masoom A, Yang Yongyi, Wernick Miles N, Yetik Imam Samil
Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL 60616, USA.
IEEE Trans Med Imaging. 2009 Jun;28(6):906-15. doi: 10.1109/TMI.2009.2012888. Epub 2009 Jan 19.
Prostate cancer is one of the leading causes of death from cancer among men in the United States. Currently, high-resolution magnetic resonance imaging (MRI) has been shown to have higher accuracy than trans-rectal ultrasound (TRUS) when used to ascertain the presence of prostate cancer. As MRI can provide both morphological and functional images for a tissue of interest, some researchers are exploring the uses of multispectral MRI to guide prostate biopsies and radiation therapy. However, success with prostate cancer localization based on current imaging methods has been limited due to overlap in feature space of benign and malignant tissues using any one MRI method and the interobserver variability. In this paper, we present a new unsupervised segmentation method for prostate cancer detection, using fuzzy Markov random fields (fuzzy MRFs) for the segmentation of multispectral MR prostate images. Typically, both hard and fuzzy MRF models have two groups of parameters to be estimated: the MRF parameters and class parameters for each pixel in the image. To date, these two parameters have been treated separately, and estimated in an alternating fashion. In this paper, we develop a new method to estimate the parameters defining the Markovian distribution of the measured data, while performing the data clustering simultaneously. We perform computer simulations on synthetic test images and multispectral MR prostate datasets to demonstrate the efficacy and efficiency of the proposed method and also provide a comparison with some of the commonly used methods.
前列腺癌是美国男性癌症死亡的主要原因之一。目前，高分辨率磁共振成像（MRI）在用于确定前列腺癌的存在时，已被证明比经直肠超声（TRUS）具有更高的准确性。由于MRI可以为感兴趣的组织提供形态和功能图像，一些研究人员正在探索多光谱MRI在引导前列腺活检和放射治疗方面的应用。然而，基于当前成像方法进行前列腺癌定位的成功率有限，这是因为使用任何一种MRI方法时，良性和恶性组织的特征空间存在重叠，并且观察者之间存在变异性。在本文中，我们提出了一种用于前列腺癌检测的新的无监督分割方法，使用模糊马尔可夫随机场（fuzzy MRFs）对多光谱MR前列腺图像进行分割。通常，硬MRF模型和模糊MRF模型都有两组需要估计的参数：图像中每个像素的MRF参数和类别参数。迄今为止，这两个参数一直是分开处理的，并以交替方式进行估计。在本文中，我们开发了一种新方法，在对测量数据进行聚类的同时，估计定义马尔可夫分布的参数。我们对合成测试图像和多光谱MR前列腺数据集进行计算机模拟，以证明所提出方法的有效性和效率，并与一些常用方法进行比较。
