Heidari Marjan, Taghizadeh Mehdi, Masoumi Hassan, Valizadeh Morteza
PhD candidate, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran.
PhD, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran.
J Biomed Phys Eng. 2021 Aug 1;11(4):527-534. doi: 10.31661/jbpe.v0i0.2103-1293. eCollection 2021 Aug.
Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher's attention, it still has some challenging problems in computer-aided diagnosis.
This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm.
In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features.
The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms.
Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.
肝脏表面及其各段的识别与精确定位对于任何外科治疗都至关重要。精确的肝脏分割算法可简化不同类型肝脏疾病的治疗规划。尽管肝脏分割已引起研究人员的关注，但在计算机辅助诊断中仍存在一些具有挑战性的问题。
本研究旨在通过自适应水流模型提取潜在肝脏区域，并通过分类算法进行最终分割。
在本实验研究中，引入了一种自动肝脏分割算法。所提出的方法基于肝脏像素的概率分布函数通过传递函数对图像进行设计，以增强肝脏区域。然后使用自适应水流模型对增强后的图像进行分割，其中降雨过程由训练图像中肝脏的位置和像素的灰度级控制。考虑一些纹理、面积和灰度级特征，通过多层感知器（MLP）神经网络对候选肝脏段进行分类。
所提出的算法能够有效地将肝脏区域与其周围器官区分开来，在超过250幅磁共振成像（MRI）测试图像上实现了完美的肝脏分割。通过对测试图像的定量评估获得了97%的准确率，这表明所提出的算法优于一些评估算法。
与像素分类相比，使用自适应水流算法进行肝脏分割并对MRI图像中的分割区域进行分类可产生更稳健、可靠的结果。
