Rene Cruz-Santiago

A new steganographic method for RGB color images using estimation of variance field in the wavelet domain

In this paper we present a steganographic method for RGB color images based on estimation of the variance field using the wavelet atomic function fup2 for adaptive steganographic applications. We introduce a preprocessing stage to allow that the resulted stego-images are less detectable by the steganalyzers increasing the embedding capacity. Experimental results show that the proposed method outperforms other methods proposed in the literature in terms of hiding capacity and image quality.

Segmentation of noisy images using the rank M-type L-filter and the fuzzy C-means clustering algorithm

In this paper we present an image processing scheme to segment noisy images based on a robust estimator in the filtering stage and the standard Fuzzy C-Means (FCM) clustering algorithm to segment the images. The main objective of paper is to evaluate the performance of the Rank M-type L-filter with different influence functions and to establish a reference base to include the filter in the objective function of FCM algorithm in a future work. The filter uses the Rank M-type (RM) estimator in the scheme of L-filter, to get more robustness in the presence of different types of noises and a combination of them. Tests were made on synthetic and real images subjected to three types of noise and the results are compared with six reference modified Fuzzy C-Means methods to segment noisy images.

Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network

The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.

Robust RML estimator - fuzzy c-means clustering algorithms for noisy image segmentation

Image segmentation is a key step for many images analysis applications. So far, there does not exist a general method to segment suitable all images, regardless if these are corrupted or noise free. In this paper, we propose to modify the Fuzzy C-means clustering algorithm and the FCM_S1 variant by using the RML-estimator. The idea to our method is to get robust clustering algorithms able to segment images with different type and levels of noises. The performance of the proposed algorithms is tested on synthetic and real images. Experimental results show that the proposed algorithms are more robust to the noise presence and more effective than the comparative algorithms.

Noise suppression by use the rank M-type L-filter in the wavelet domain

In this paper we present the Wavelet Domain Vector Median M-type L-filter (WDVMML) for the removal of impulsive and speckle noise in color image processing applications. The proposed filter uses the robust RM-estimator in the filtering scheme of L-filter. We adapt the RM L-filter to work in wavelet domain by use the Daubichies wavelet dB4. Simulation results have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and detail preservation.