医用内窥镜图像校正的BP神经网络方法

Distortion Correction of Medical Endoscopic Images Using BP Neural Network

用传统的畸变校正法对内窥镜图像进行校正存在畸变模型复杂、计算量大、易产生误差等问题.该文利用标准模板,提取样本点在标准图像和内窥镜拍摄的畸变图像中的坐标,分别作为BP神经网络训练的输入和目标,通过神经网络的训练拟合成像镜头的畸变模型,从而确定标准图像和畸变图像上像素点的位置关系.再通过图像插值的方法进行图像恢复,实现图像校正.实验结果表明该方法简单有效,具有精确性.

Traditional distortion correction methods for medical endoscopic images have problems such as complication of distortion modeling, high computation complexity, and susceptibility to errors. The proposed method uses a correction template. Sample coordinates are extracted from the template, and the distorted template image acquired by the endoscope. These two kinds of extracted coordinates serve respectiveIy as inputs and targets for training to give the distortion model. Based on the distortion model, a one-to-one correspondence between pixels on the ideal and distorted images is set up. Image correction can then be accomplished by using interpolation. The experimental results show that the proposed method is simple, effective and accurate.