Computer-aided diagnosis (CADx) has become vital in medical imaging, but automated systems often struggle to replicate the nuanced process of clinical interpretation. Expert diagnosis requires a comprehensive analysis of how abnormalities relate to each other across various views and time points, but current multi-view CADx methods frequently overlook these complex dependencies. Specifically, they fail to model the crucial relationships within a single view and the dynamic changes lesions exhibit across different views. This limitation, combined with the common challenge of incomplete data, greatly reduces their predictive reliability. To address these gaps, we reframe the diagnostic task as one of relationship modeling and propose GIIM, a novel graph-based approach. Our framework is uniquely designed to simultaneously capture both critical intra-view dependencies between abnormalities and inter-view dynamics. Furthermore, it ensures diagnostic robustness by incorporating specific techniques to effectively handle missing data, a common clinical issue. We demonstrate the generality of this approach through extensive evaluations on diverse imaging modalities, including CT, MRI, and mammography. The results confirm that our GIIM model significantly enhances diagnostic accuracy and robustness over existing methods, establishing a more effective framework for future CADx systems.

This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the observed radar signals, a joint distribution of the observed data and latent variables is formulated by incorporating two important beliefs: low-dimensional structure of wall reflections and joint sparsity among channel images. These priors are modeled through probabilistic distributions whose hyperparameters are treated with a full Bayesian formulation. Furthermore, the paper presents a variational Bayesian inference algorithm that captures wall clutter and provides channel images as full posterior distributions. Experimental results on real data show that the proposed model is very effective at removing wall clutter and enhancing target localization.

This paper addresses the problem of wall clutter mitigation and image reconstruction for through-wall radar imaging (TWRI) of stationary targets by seeking a model that incorporates low-rank (LR), joint sparsity (JS), and total variation (TV) regularizers. The motivation of the proposed model is that LR regularizer captures the low-dimensional structure of wall clutter; JS guarantees a small fraction of target occupancy and the similarity of sparsity profile among channel images; TV regularizer promotes the spatial continuity of target regions and mitigates background noise. The task of wall clutter mitigation and target image reconstruction is formulated as an optimization problem comprising LR, JS, and TV regularization terms. To handle this problem efficiently, an iterative algorithm based on the forward-backward proximal gradient splitting technique is introduced, which captures wall clutter and yields target images simultaneously. Extensive experiments are conducted on real radar data under compressive sensing scenarios. The results show that the proposed model enhances target localization and clutter mitigation even when radar measurements are significantly reduced.

This paper proposes a low-rank and sparse regularized optimization model to address the problem of wall clutter mitigation, stationary, and moving target indications using through-wall radar. The task of wall clutter suppression and target image reconstruction is formulated as a nuclear and ℓ1penalized least squares optimization problem in which the nuclear-norm term enforces for a low-rank wall clutter matrix and the ℓ1-norm term promotes the sparsity of the target images. An iterative algorithm based on the proximal gradient technique is introduced to solve the optimization problem. The solution comprises the wall clutter and images of stationary and moving targets. Experiments are conducted on real radar data under compressive sensing scenarios. The results show that the proposed model is very effective at removing unwanted wall clutter, reconstructing stationary targets, and capturing moving targets.

This paper presents a low-rank matrix completion approach to tackle the problem of wall clutter mitigation for through-wall radar imaging in the compressive sensing context. In particular, the task of wall clutter removal is reformulated as a matrix completion problem in which a low-rank matrix containing wall clutter is reconstructed from compressive measurements. The proposed model regularizes the low-rank prior of the wall-clutter matrix via the nuclear norm, casting the wall-clutter mitigation task as a nuclear-norm penalized least squares problem. To solve this optimization problem, an iterative algorithm based on the proximal gradient technique is introduced. Experiments on simulated full-wave electromagnetic data are conducted under compressive sensing scenarios. The results show that the proposed matrix completion approach is very effective at suppressing unwanted wall clutter and enhancing the targets.

Compressed sensing techniques have been applied to through-the-wall radar imaging (TWRI) and multipolarization TWRI for fast data acquisition and enhanced target localization. The studies so far in this area have either assumed effective wall clutter removal prior to image formation or performed signal estimation, wall clutter mitigation, and image formation independently. This paper proposes a low-rank and sparse imaging model for jointly addressing the problem of wall clutter mitigation and image formation in multichannel TWRI. The proposed model exploits two important structures of through-wall radar signals: low-rank structure of the wall reflections and jointly-sparse structure among the different polarization images. The task of removing wall clutter and reconstructing multichannel images of the same scene behind-the-wall is formulated as a regularized least squares problem, where low-rank regularization is enforced for the wall components, and joint-sparsity penalty is imposed on channel images. To solve the optimization problem, an iterative algorithm based on the proximal gradient technique is introduced, which simultaneously estimates the wall interferences and yields multichannel images of the indoor targets. Experiments on real and simulated radar data are conducted under full measurements and compressive sensing scenarios. The results show that the proposed model is very effective at removing unwanted wall clutter and enhancing the stationary targets, even under considerable reduction in measurements.

This paper introduces a joint low-rank and sparsity-based model to address the problem of wall-clutter mitigation in compressed through-the-wall radar imaging. The proposed model is motivated by two observations that wall reflections reside in a low-rank subspace, and target signals tend to be sparse. In the proposed approach, the task of segregating target returns from wall reflections is formulated as a joint low-rank and sparsity constrained optimization problem. Here, the low rank constraint is imposed on the wall component and the sparsity constraint is used to model the target component. An iterative soft thresholding algorithm is developed to estimate a low-rank matrix of wall clutter and a sparse matrix of target reflections from a reduced measurement set. Once the wall and target components are estimated, the target signals are used for scene reconstruction. Experimental evaluation was conducted using real radar data. The results show that the proposed model is very effective at removing wall clutter and reconstructing the image of behind-the-wall targets from reduced measurements.

This paper addresses the problem of scene reconstruction, incorporating wall-clutter mitigation, for compressed multi-view through-the-wall radar imaging. We consider the problem where the scene is sensed using different reduced sets of frequencies at different antennas. A joint Bayesian sparse recovery framework is first employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and correlations between antenna signals. Following joint signal coefficient estimation, a subspace projection technique is applied to segregate the target coefficients from the wall contributions. Furthermore, a multitask linear model is developed to relate the target coefficients to the scene, and a composite scene image is reconstructed by a joint Bayesian sparse framework, taking into account the inter-view dependencies. Experimental results show that the proposed approach improves reconstruction accuracy and produces a composite scene image in which the targets are enhanced and the background clutter is attenuated.

This paper addresses the problem of wall clutter mitigation in compressed sensing through-the-wall radar imaging, where a different set of frequencies is sensed at different antenna locations. A joint Bayesian sparse approximation framework is first employed to reconstruct all the signals simultaneously by exploiting signal sparsity and correlations between antenna signals. This is in contrast to previous approaches where the signal at each antenna location is reconstructed independently. Furthermore, to promote sparsity and improve reconstruction accuracy, a sparsifying wavelet dictionary is employed in the sparse signal recovery. Following signal reconstruction, a subspace projection technique is applied to remove wall clutter, prior to image formation. Experimental results on real data show that the proposed approach produces significantly higher reconstruction accuracy and requires far fewer measurements for forming high-quality images, compared to the single-signal compressed sensing model, where each antenna signal is reconstructed independently.