This study investigates a novel CT/MR spine image fusion algorithm based on graph cuts. This algorithm allows physicians to visually assess corresponding soft tissue and bony detail on a single image eliminating mental alignment and correlation needed when both CT and MR images are required for diagnosis. We state the problem as a discrete multilabel optimization of an energy functional that balances the contributions of three competing terms: (1) a squared error, which encourages the solution to be similar to the MR input, with a preference to strong MR edges; (2) a squared error, which encourages the solution to be similar to the CT input, with a preference to strong CT edges; and (3) a prior, which favors smooth solutions by encouraging neighboring pixels to have similar fused-image values. We further introduce a transparency-labeling formulation, which significantly reduces the computational load. The proposed graph-cut fusion guarantees nearly global solutions, while avoiding the pix elation artifacts that affect standard wavelet-based methods. We report several quantitative evaluations/comparisons over 40 pairs of CT/MR images acquired from 20 patients, which demonstrate a very competitive performance in comparisons to the existing methods. We further discuss various case studies, and give a representative sample of the results.

/pdf/spine-image-fusion-via-graph-cuts-17rdopds3l.pdf

Spine Image Fusion Via Graph Cuts

Multi-modal bioelectrical signal fusion analysis based on different acquisition devices and scene settings: Overview, challenges, and novel orientation

In recent years, automated retinal vessel segmentation has become especially essential for the early detection of some ophthalmological and cardiovascular diseases. In this paper, we have presented a new retinal vessel segmentation method via discriminative dictionary learning using fusion of multiple features, which is able to capture both thick and thin vessel structures. In the training stage, we employ six different enhancement algorithms to obtain multiple complementary features that contain rich vascular information. Then, the manually annotated ground-truth vessels are classified into thick or thin vessels as the label information, and the label consistent KSVD based framework is applied to train the dictionary for vessel segmentation. In the testing stage, comprehensive experiments are conducted on three datasets to measure segmentation performance with eight representative evaluation metrics. The average sensitivity reaches 0.7915, 0.7560 and 0.7202 respectively, suggesting that our method can segment tiny vascular structures well.

Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features

We propose an adaptive regularized algorithm for remote sensing image fusion based on variational methods. In the algorithm, we integrate the inputs using a “grey world” assumption to achieve visual uniformity. We propose a fusion operator that can automatically select the total variation (TV)–L1 term for edges and L2-terms for non-edges. To implement our algorithm, we use the steepest descent method to solve the corresponding Euler–Lagrange equation. Experimental results show that the proposed algorithm achieves remarkable results.

Adaptive regularized scheme for remote sensing image fusion

Image fusion is an imaging technique to visualize information from multiple imaging sources by one single image, which is widely used in remote sensing, medical imaging etc. In this work, we study two variational approaches to image fusion which are closely related to the standard TV-L2 and TV-L1 image approximation methods. We investigate their convex optimization formulations, under the perspective of primal and dual, and propose their associated new image decomposition models. In addition, we consider the TV-L1 based image fusion approach and study the specified problem of fusing two discrete-constrained images f1(x) ∈ L1 and f2(x) ∈ L2, where L1 and L2 are the sets of linearly-ordered discrete values. We prove that the TV-L1 based image fusion actually gives rise to the exact convex relaxation to the corresponding nonconvex image fusion constrained by the discrete-valued set u(x) ∈ L1 ∪ L2. This extends the results for the global optimization of the discrete-constrained TV-L1 image approximation [7, 33] to the case of image fusion. As a big numerical advantage of the two proposed dual models, we show both of them directly lead to new fast and reliable algorithms, based on modern convex optimization techniques. Experiments of medical imaging, remote sensing and multi-focusing visibly show the qualitive differences between the two studied variational models of image fusion. We also apply the new variational approaches to fusing 3D medical images.

Efficient Convex Optimization Approaches to Variational Image Fusion

Lipoma arborescens is a rare non-neoplastic condition that affects the synovial lining of joints and bursae accounting for less than 1% of all lipomatous lesions. Characterized by villous proliferation of the synovium, it is an uncommon cause of intra/periarticular mass presenting as a painless, slowly progressive longstanding swelling, and is sometimes accompanied by intermittent monoarticular effusions. We describe a rare case of bilateral lipoma arborescens in the bicipitoradial bursae in a young male referred for MRI evaluation of spontaneous bilateral elbow swelling. We chose to bring this case to light because of the rare simultaneous involvement of the bicipitoradial bursae bilaterally and the role of MR in providing a definite diagnosis, hence obviating the need for biopsy and avoiding consideration of other complex intra/periarticular masses.

Synchronous bilateral lipoma arborescens of bicipitoradial bursa—a rare entity

In an attempt to improve the visualisation techniques for diagnosis and treatment of musculoskeletal injuries, we present a novel method for a pixel-wise fusion of CT and MR images. We focus on the spine and its related diseases including osteophyte growth, degenerate disc disease and spinal stenosis. This will have benefit to the 50-75% of people who suffer from back pain, which is the reason for 1.8% of all hospital stays in the United States. 1 Pre-registered CT and MR image pairs were used. Rigid registration was performed based on soft tissue correspondence. A pixel-wise image fusion algorithm was designed to combine CT and MR images into a single image. This was accomplished by minimizing an energy functional using a graph cut approach. The functional was formulated to balance the similarity between the resultant image and the CT image as well as between the resultant image and the MR image. Furthermore the variational smoothness of the resultant image was considered in the energy functional (to enforce natural transitions between pixels). The results have been validated based on the amount of significant detail preserved in the final fused image. Based on bone cortex and disc / spinal cord areas, 95% of the relevant MR detail and 85% of the relevant CT detail was preserved. This work has the potential to aid in patient diagnosis, surgery planning and execution along with post operative follow up.

/pdf/pixel-level-image-fusion-for-medical-imaging-an-energy-333ywlmai0.pdf

Greg Garvin

Papers

Efficient Convex Optimization Approaches to Variational Image Fusion

Synchronous bilateral lipoma arborescens of bicipitoradial bursa—a rare entity

Pixel level image fusion for medical imaging: an energy minimizing approach