Wavelet-domain Hidden Markov Tree Model Research Articles

The Application of Wavelet-Domain Hidden Markov Tree Model in Diabetic Retinal Image Denoising.

The wavelet-domain Hidden Markov Tree Model can properly describe the dependence and correlation of fundus angiographic images’ wavelet coefficients among scales. Based on the construction of the fundus angiographic images Hidden Markov Tree Models and Gaussian Mixture Models, this paper applied expectation-maximum algorithm to estimate the wavelet coefficients of original fundus angiographic images and the Bayesian estimation to achieve the goal of fundus angiographic images denoising. As is shown in the experimental result, compared with the other algorithms as mean filter and median filter, this method effectively improved the peak signal to noise ratio of fundus angiographic images after denoising and preserved the details of vascular edge in fundus angiographic images.

Wavelet-domain Hidden Markov Tree Model Approach to Fusion of Multispectral and Panchromatic Images

Wavelet-domain Hidden Markov Tree Model Approach to Fusion of Multispectral and Panchromatic Images

Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides

We aim at segmenting very large images of histopathology virtual slides with an heterogeneous and complex content. To this end, we propose a multiscale framework for texture-based color image segmentation. The core of the method is based on a wavelet-domain hidden Markov tree model and a pairwise classifiers design and selection. The classifier selection is founded on a study of the influence of the hyperparameters of the method used. Over the testing set, majority vote was found to be the best way of combining outputs of the selected classifiers. The method is applied to the segmentation of various types of ovarian carcinoma stroma, on very large virtual slides. This is the first time such a segmentation is tested. The segmentation results are presented and discussed.