Telematics (CA), Engineering Division (DICIS), Campus Irapuato-Salamanca, University of Guanajuato, Carr. Salamanca-Valle de Santiago km 3.5 + 1.8km, Comunidad de Palo Blanco, Salamanca, 36885, Gto., Mexico.
Life Sciences Division (DICIVA), Campus Irapuato-Salamanca, University of Guanajuato, Carr. Irapuato-Silao km 9, ap 311, Ex Hacienda el Copal, Irapuato, 36500, Gto., Mexico.
Comput Biol Med. 2017 Dec 1;91:69-79. doi: 10.1016/j.compbiomed.2017.10.003. Epub 2017 Oct 7.
Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately.
脑肿瘤分割是临床中的常规流程，可为诊断和治疗计划提供有用的信息。由于目前生成的医学数据量很大，因此由医生或放射科医生执行的手动分割是一项耗时的任务。因此，需要自动分割方法，近年来已经提出了几种方法，包括基于局部区域的主动轮廓模型 (LRACM)。有许多流行的 LRACM，但它们中的每一个都有优点和缺点。本文提出了一种基于图像内容的自动选择 LRACM 及其在脑肿瘤分割中的应用。为此，提出了一种选择三种 LRACM（局部高斯分布拟合 (LGDF)、局部 Chan-Vese (C-V) 和带背景强度补偿的局部主动轮廓模型 (LACM-BIC)）之一的框架。提取了 12 个视觉特征来正确选择可能处理给定输入图像的方法。该系统基于监督方法。专门应用于磁共振成像 (MRI) 图像的实验表明，所提出的系统能够正确选择合适的 LRACM 来处理特定的图像。因此，与三个 LRACM 分别相比，选择框架的准确性更好。
