Analysis and Modeling of Statistical Properties of FMDFB Subband Coefficients

Fast Multiscale Directional Filter Bank (FMDFB) is an image representation scheme used in several image processing applications. The statistical nature of the FMDFB subbands is analyzed, and a mathematical model of FMDFB coefficients is proposed. Experimental results are justified by goodness-of-fit tests

Novel CBIR System Based on Ripplet Transform Using Interactive Neuro-Fuzzy Technique

Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 £ 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant

The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices

Directional wavelet based features for colonic polyp classification

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.(VLID)223912

Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method