Tao Wan

Segmentation-Driven Image Fusion Based on Alpha-Stable Modeling of Wavelet Coefficients

A novel region-based image fusion framework based on multiscale image segmentation and statistical feature extraction is proposed. A dual-tree complex wavelet transform (DT-CWT) and a statistical region merging algorithm are used to produce a region map of the source images. The input images are partitioned into meaningful regions containing salient information via symmetric alpha-stable (S alphaS) distributions. The region features are then modeled using bivariate alpha-stable (B alphaS) distributions, and the statistical measure of similarity between corresponding regions of the source images is calculated as the Kullback-Leibler distance (KLD) between the estimated B alphaS models. Finally, a segmentation-driven approach is used to fuse the images, region by region, in the complex wavelet domain. A novel decision method is introduced by considering the local statistical properties within the regions, which significantly improves the reliability of the feature selection and fusion processes. Simulation results demonstrate that the bivariate alpha-stable model outperforms the univariate alpha-stable and generalized Gaussian densities by not only capturing the heavy-tailed behavior of the subband marginal distribution, but also the strong statistical dependencies between wavelet coefficients at different scales. The experiments show that our algorithm achieves better performance in comparison with previously proposed pixel and region-level fusion approaches in both subjective and objective evaluation tests.

Compressive image fusion

Compressive sensing (CS) has received a lot of interest due to its compression capability and lack of complexity on the sensor side. In this paper, we present a study of three sampling patterns and investigate their performance on CS reconstruction. We then propose a new image fusion algorithm in the compressive domain by using an improved sampling pattern. There are few studies regarding the applicability of CS to image fusion. The main purpose of this work is to explore the properties of compressive measurements through different sampling patterns and their potential use in image fusion. The study demonstrates that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the multiresolution (MR) domain. The simulations show that the proposed CS-based image fusion algorithm provides promising results.

Statistical Multiscale Image Segmentation via Alpha-Stable Modeling

This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by symmetric alpha-stable (SalphaS) distributions. These features are efficiently combined with the dominant color feature to perform automatic segmentation. First, the image is roughly segmented into textured and nontextured regions using the dual-tree complex wavelet transform (DT-CWT) with the sub-band coefficients modeled as SalphaS random variables. A mul-tiscale segmentation is then applied to the resulting regions, according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring the Kullback-Leibler distance (KLD) between estimated SalphaS models for the neighboring segments. Experiments show that our algorithm achieves superior segmentation results in comparison with existing state-of-the-art image segmentation algorithms.

Context enhancement through image fusion: A multiresolution approach based on convolution of cauchy distributions

A novel context enhancement technique is presented to automatically combine images of the same scene captured at different times or seasons. A unique characteristic of the algorithm is its ability to extract and maintain the meaningful information in the enhanced image while recovering the surrounding scene information by fusing the background image. The input images are first decomposed into multiresolution representations using the Dual-Tree Complex Wavelet Transform (DT-CWT) with the subband coefficients modelled as Cauchy random variables. Then, the convolution of Cauchy distributions is applied as a probabilistic prior to model the fused coefficients, and the weights used to combine the source images are optimised via Maximum Likelihood (ML) estimation. Finally, the importance map is produced to construct the composite approximation image. Experiments show that this new model significantly improves the reliability of the feature selection and enhances fusion process.

Multiscale Color-Texture Image Segmentation with Adaptive Region Merging

A novel multiscale image segmentation algorithm is presented, which is based on the dominant color and homogeneous texture features (HTF) that are adopted in the MPEG-7 standard. These features are efficiently combined to perform the automatic segmentation. First, the image is roughly segmented into textured and nontextured regions using Gabor decomposition. A multiscale segmentation is then applied to the resulting regions, according to the local texture feature. Finally, a precise boundary refinement procedure is employed to accurately determine the boundaries between textured and nontextured regions. A novel region merging algorithm is introduced with a simple and effective segment classification by using HTF to deal with the over-segmentation problem. Experiments show that our algorithm provides an improved performance compared with JSEG and a watershed algorithm.

Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model

PURPOSE To develop a new spatio-temporal texture (SpTeT) based method for distinguishing vulnerable versus stable atherosclerotic plaques on DCE-MRI using a rabbit model of atherothrombosis. METHODS Aortic atherosclerosis was induced in 20 New Zealand White rabbits by cholesterol diet and endothelial denudation. MRI was performed before (pretrigger) and after (posttrigger) inducing plaque disruption with Russells-viper-venom and histamine. Of the 30 vascular targets (segments) under histology analysis, 16 contained thrombus (vulnerable) and 14 did not (stable). A total of 352 voxel-wise computerized SpTeT features, including 192 Gabor, 36 Kirsch, 12 Sobel, 52 Haralick, and 60 first-order textural features, were extracted on DCE-MRI to capture subtle texture changes in the plaques over the course of contrast uptake. Different combinations of SpTeT feature sets, in which the features were ranked by a minimum-redundancy-maximum-relevance feature selection technique, were evaluated via a random forest classifier. A 500 iterative 2-fold cross validation was performed for discriminating the vulnerable atherosclerotic plaque and stable atherosclerotic plaque on per voxel basis. Four quantitative metrics were utilized to measure the classification results in separating between vulnerable and stable plaques. RESULTS The quantitative results show that the combination of five classes of SpTeT features can distinguish between vulnerable (disrupted plaques with an overlying thrombus) and stable plaques with the best AUC values of 0.9631 ± 0.0088, accuracy of 89.98% ± 0.57%, sensitivity of 83.71% ± 1.71%, and specificity of 94.55% ± 0.48%. CONCLUSIONS Vulnerable and stable plaque can be distinguished by SpTeT based features. The SpTeT features, following validation on larger datasets, could be established as effective and reliable imaging biomarkers for noninvasively assessing atherosclerotic risk.

An application of compressive sensing for image fusion

Compressive sensing(CS) has inspired significant interest because of its compressive capability and lack of complexity on the sensor side. In this paper, we present a study of three sampling patterns and investigate their performance on CS reconstruction. We then propose a new image fusion algorithm in the compressive domain by using an improved sampling pattern. There are few studies regarding the applicability of CS to image fusion. The main purpose of this work is to explore the properties of compressive measurements through different sampling patterns and their potential use in image fusion. The study demonstrates that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the multiresolution (MR) domain. The simulations show that the proposed CS-based image fusion algorithm provides promising results.

A learning based fiducial-driven registration scheme for evaluating laser ablation changes in neurological disorders

In this work, we present a novel learning based fiducial driven registration (LeFiR) scheme which utilizes a point matching technique to identify the optimal configuration of landmarks to better recover deformation between a target and a moving image. Moreover, we employ the LeFiR scheme to model the localized nature of deformation introduced by a new treatment modality - laser induced interstitial thermal therapy (LITT) for treating neurological disorders. Magnetic resonance (MR) guided LITT has recently emerged as a minimally invasive alternative to craniotomy for local treatment of brain diseases (such as glioblastoma multiforme (GBM), epilepsy). However, LITT is currently only practised as an investigational procedure world-wide due to lack of data on longer term patient outcome following LITT. There is thus a need to quantitatively evaluate treatment related changes between post- and pre-LITT in terms of MR imaging markers. In order to validate LeFiR, we tested the scheme on a synthetic brain dataset (SBD) and in two real clinical scenarios for treating GBM and epilepsy with LITT. Four experiments under different deformation profiles simulating localized ablation effects of LITT on MRI were conducted on 286 pairs of SBD images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid (UniG) placement scheme, a speeded-up robust features (SURF) based method, and a scale-invariant feature transform (SIFT) based method as well as a generic free-form deformation (FFD) approach. The LeFiR method achieved average 90% improvement in recovering the local deformation compared to 82% for the uniform grid placement, 62% for the SURF based approach, and 16% for the generic FFD approach. On the real GBM and epilepsy data, the quantitative results showed that LeFiR outperformed UniG by 28% improvement in average.

A novel point-based nonrigid image registration scheme based on learning optimal landmark configurations

Image registration plays an increasingly important role in the field of medical image processing given the plurality of images often acquired from different sensors, time points, or viewpoints. Landmark-based registration schemes represent the most popular class of registration methods due to their simplicity and high accuracy. Previous studies have shown that these registration schemes are sensitive to the number and location of landmarks. Identifying important landmarks to perform an accurate registration remains a very challenging task. Current landmark selection methods, such as feature-based approaches, focus on optimization of global transformation and may have poor performance in recovering local deformation, e.g. subtle tissue changes caused by tumor resection, making them inappropriate for registering pre- and post-surgery images as a small cancerous region will be deformed after removing a tumor. In this work, a novel method is introduced to estimate optimal landmark configurations. An important landmark configuration that will be used as a training landmark set was learned for an image pair with a known deformation. This landmark configuration can be considered as a collection of discrete points. A generic transformation matrix between a pair of training landmark sets with different deformation locations was computed via an iterative close point (ICP) alignment technique. A new landmark configuration was determined by simply transforming the training landmarks to the current displacement location while preserving the topological structure of the configuration of landmarks. Two assumptions are made: 1) In a new pair of images the deformation is approximately the same size and has only been spatially relocated in the image, and that by a simple affine transformation one can identify the optimal configuration on this new pair of images; and 2) The deformation is of similar size and shape on the original pair of images. These are reasonable assumptions in many cases where one seeks to register tumor images at multiple time points following application of therapy and to evaluate changes in tumor size. The experiments were conducted on 286 pairs of synthetic MRI brain images. The training landmark configurations were obtained through 2000 iterations of registration where the points with consistently best registration performance were selected. The estimated landmarks greatly improved the quality metrics compared to a uniform grid placement scheme and a speeded-up robust features (SURF) based method as well as a generic free-form deformation (FFD) approach. The quantitative results showed that the new landmark configuration achieved 95% improvement in recovering the local deformation compared to 89% for the uniform grid placement, 79% for the SURF-based approach, and 10% for the generic FFD approach.

Statistical multisensor image segmentation in complex wavelet domains

We propose an automated image segmentation algorithm for segmenting multisensor images, in which the texture features are extracted based on the wavelet transform and modeled by generalized Gaussian distribution (GGD). First, the image is roughly segmented into textured and non-textured regions in the dual-tree complex wavelet transform (DT-CWT) domain. A multiscale segmentation is then applied to the resulting regions according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring a Kullback-Leibler distance (KLD) between estimated GGD models for the neighboring segments. Experiments demonstrate that our algorithm achieves superior segmentation results.