title: Segmentation of medical ultrasonic image using hybrid neural network.
abstract: Objective. To solve one of the most difficult problems in multi-dimensional reconstruction of medical ultrasonic images: image segmentation. Method. A new segmental method based on hybrid neural network was presented in this paper. The hybrid neural network comprised two phases. The first phase was Kohonens self-organization neural network, which was used to segment and label the image coarsely. The feature vectors of those pixels within a specified distance from the cluster centers were employed to train the second phase--a three-layer perception network using back-propagation (BP) technique. Then the trained BP network was used to label every pixel of the image. In the end, a post-processing stage was used to remove the small isolated points and smooth out the contours of the segmented image. Result. The segmented image had smooth continuous edges, few noises or speckles, and the contour of ventricle was clear and accurate. Conclusion. Our method could segment the ultrasonic images accurately and effectively, and had a lot of advantages compared to traditional methods. The unsupervised segmentation problems could be solved using supervised methods.
authors.affiliation: The Center for Biomedical Engineering, Sichuan University, Chengdu, China.
authors.forename: T F
authors.initials: TF
authors.lastname: Wang
authors.forename: D Y
authors.initials: DY
authors.lastname: Li
authors.forename: C Q
authors.initials: CQ
authors.lastname: Zheng
authors.forename: Y
authors.initials: Y
authors.lastname: Zheng
mesh_terms: D002320:Cardiovascular Physiological Phenomena; D004452:Echocardiography / Q000379:methods*; D017709:Fractals; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer