Chen E L, Chung P C, Chen C L, Tsai H M, Chang C I
Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C.
IEEE Trans Biomed Eng. 1998 Jun;45(6):783-94. doi: 10.1109/10.678613.
Computed tomography (CT) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, a CT liver image diagnostic classification system is presented which will automatically find, extract the CT liver boundary and further classify liver diseases. The system comprises a detect-before-extract (DBE) system which automatically finds the liver boundary and a neural network liver classifier which uses specially designed feature descriptors to distinguish normal liver, two types of liver tumors, hepatoma and hemageoma. The DBE system applies the concept of the normalized fractional Brownian motion model to find an initial liver boundary and then uses a deformable contour model to precisely delineate the liver boundary. The neural network is included to classify liver tumors into hepatoma and hemageoma. It is implemented by a modified probabilistic neural network (PNN) [MPNN] in conjunction with feature descriptors which are generated by fractal feature information and the gray-level co-occurrence matrix. The proposed system was evaluated by 30 liver cases and shown to be efficient and very effective.
计算机断层扫描（CT）图像已被广泛用于肝脏疾病诊断。在过去几年中，设计和开发计算机辅助图像处理技术以帮助医生改善诊断受到了广泛关注。本文提出了一种CT肝脏图像诊断分类系统，该系统将自动找到、提取CT肝脏边界并进一步对肝脏疾病进行分类。该系统包括一个先检测后提取（DBE）系统，该系统自动找到肝脏边界，以及一个神经网络肝脏分类器，该分类器使用专门设计的特征描述符来区分正常肝脏、两种类型的肝脏肿瘤，即肝癌和肝血管瘤。DBE系统应用归一化分数布朗运动模型的概念来找到初始肝脏边界，然后使用可变形轮廓模型精确描绘肝脏边界。包含神经网络以将肝脏肿瘤分为肝癌和肝血管瘤。它由改进的概率神经网络（PNN）[MPNN]结合由分形特征信息和灰度共生矩阵生成的特征描述符来实现。所提出的系统通过30例肝脏病例进行了评估，结果表明该系统高效且非常有效。
