关键词: 人工神经网络, 活动轮廓模型, 医学图像分割

Abstract:

This paper proposed a new image segmentation scheme to solve the manual segmentation by and the poor accuracy of existing image segmentation methods. Firstly, the massive-training artificial neural networks (MTANNs) are employed to suppress bones in the lungs, using artificial neural network trained on a large scale of standard chest radiograph of the ribs, clavicle, such as inhibition of bone structure. Then active contour model (ACM) based on region, or the Snake Mode, is adopted to segment correctly images with non-uniform brightness. A comparison with images manually segmented by medical personnel and rated by radiologists shows that the average score of the original images is 20 points while that by our improved segmentation method is 34 points.

Key words: artificial neural network, active contour model, medical image segmentation