Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Biomed Res Int. 2017;2017:5207685. doi: 10.1155/2017/5207685. Epub 2017 Sep 26.
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy -means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.
基于改进的模糊均值（FCM）和图割的 CT 容积三维肝脏肿瘤自动分割
三维（3D）肝脏肿瘤分割是计算机辅助诊断、治疗计划和肝癌监测的前提。尽管经过多年的研究，3D 肝脏肿瘤分割仍然是一项具有挑战性的任务。本文提出了一种基于改进的模糊均值（FCM）和图割的高效半自动 CT 容积肝脏肿瘤分割方法。该方法使用置信度连通区域生长算法提取感兴趣的肿瘤体积，仅用一个种子点即可提取肿瘤体积，从而降低计算成本。然后，自动标记初始的前景/背景区域，并在图割分割中引入具有空间信息的核 FCM 以提高分割精度。该方法在包含各种大小肝脏肿瘤的 15 个 CT 容积的公共临床数据集（3Dircadb）上进行了评估。我们实现了平均体积重叠误差（VOE）为 29.04%，骰子相似系数（DICE）为 0.83，每个肿瘤的平均处理时间为 45 秒。实验结果表明，该方法对 3D 肝脏肿瘤的分割具有较高的准确性，同时处理时间也有所减少。
