Kachouie Nezamoddin N, Fieguth Paul
Department of Systems Design Engineering, University of Waterloo, 200 University Ave. West, Waterloo, Ontario, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5605-8. doi: 10.1109/IEMBS.2007.4353617.
Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local Binary Pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.
前列腺癌的诊断和治疗依赖于经直肠超声（TRUS）前列腺图像的分割。由于前列腺边界模糊、斑点噪声以及灰度级范围较短，这是一项具有挑战性且困难的任务。数字成像技术的进步使得获取大量TRUS前列腺图像成为可能，因此对自动分割系统有相当大的需求。局部二值模式（LBP）已用于纹理分割和分析。尽管它在纹理分类方面表现出良好的性能，但尚未被考虑用于TRUS前列腺分割。在本文中，我们引入了一种医学纹理局部二值模式算子，专为医学成像应用而设计，在这些应用中，不同的组织或微生物可能具有极其微弱的潜在纹理，这使得普通纹理分析方法无法或很难对它们进行分类。在所提出的方法中，基于医学纹理局部二值模式算子来控制水平集轮廓的变形。
