Influence of multiscale denoising on Bayesian constrained spectral method in segmentation of noisy medical images

The spectral method of medical images segmentation that is constrained by Bayesian inference on initial edge map detection is introduced and briefly characterized. The accuracy of the method depends on the noise that affects the data. Gaussian noise model is constructed and a method for noisy data multiscale wavelet decomposition and denoising is applied. The proposed segmentation method is tested for denoised cardiac ultrasonic data and its performance is compared for different noise clipping values. It is found that proposed denoising scheme has significant influence on the accuracy of segmentation in medical imaging.