Ultrasound Image Segmentation by Using Wavelet Transform and Self-Organizing Neural Network

Neural Information Processing – Letters and Reviews. 2006. Vol. 10. No. 8-9. P. 183–191.

Ишджан З., Kurnaz M. N.

This paper presents an improved incremental self-organizing map (I2SOM) network that uses automatic threshold (AT) value for the segmentation of ultrasound (US) images. In order to show the validity of proposed scheme, it has been compared with Kohonen’s SOM. Two-dimensional (2D) fast Fourier transform (FFT) and 2D continuous wavelet transform (CWT) were computed in order to form the feature vectors of US bladder and phantom images. In this study, it is observed that the proposed automatic threshold scheme for ISOM network has significantly eliminated the former ISOM network’s threshold problem for US images. This improvement enhances the robustness of ISOM algorithm. Obtained results show that ISOM with AT value has similar segmentation performance with Kohonen’s network. 

Ключевые слова: neural networks, Continuous wavelet transform, Segmentation of ultrasound images, incremental self-organizing map, Fast Fourier Transform