基于贝叶斯分层混合模型的X线胸片图像病例分析

X线胸片是检查肺部疾病的重要手段,其图像分割对于分析肺部疾病具有重要作用,可为医师的诊断提供依据。本研究使用Python软件基于贝叶斯分层混合模型对X线胸片进行分割,通过对其进行可逆跳转马尔可夫链蒙特卡罗(RJMCMC)方法分析,提取X线胸片的图像特征,实现该图像的自动分割,且分割效果与原始图像特征相吻合,最终,结合图像分析结果,可对X线胸片进行病理分析,为智能医学提供理论知识和实践指引。

Case Analysis of Chest Image Based on Bayesian Layered Hybrid Model

Until now, it is a significant method to examine the lung diseases by radiograph technology. The segmentation of radiograph images plays an imperative role in the analysis of lung diseases and provides a basis for doctors’ diagnosis. In this paper, we employed the Bayesian layered hybrid model to segment lung X-rays by Python software. By analyzing the reversible jump Markov chain Monte Carlo(RJMCMC) method and extracting the features of the lung radiograph according to the characteristics of the image, the image is successfully segmented, and the segmentation effect is in good agreement with the original image features. Combined with the results of image analysis, the corresponding lung X-ray photographs were pathologically analyzed. The results of this paper provide theoretical knowledge and practical guidance for smart medicine.