Diabetic foot ulcers represent a significant health issue, and daily wound care is necessary for wound healing to occur. The goal of this research is to create a smart phone based wound image analysis system for people with diabetes to track the healing process of chronic ulcers and wounds. This system has been implemented on an Android smart phone in collaboration with a PC (or embedded PC). The wound image is captured by the smart phone camera and transmitted to the PC via Wi-Fi for image processing. The PC converts the JPEG image to bitmap format, then performs boundary segmentation on the wound in the image. The segmentation is done with a particular implementation of the level set algorithm, the <i>distance regularized level set evolution</i> (DRLSE) method, which eliminates the need for re-initialization of level set function. Next, an assessment of the wound healing is performed with color segmentation within the boundaries of the wound image, by applying the K-Mean color clustering algorithm based on the red-yellow-black (RYB) evaluation model. Finally, the results are re-formatted to JPEG, transmitted back to the smart phone and displayed. To accelerate the wound image segmentation, we have implemented the DRLSE method on the GPU and CPU cooperative hardware platform in data-parallel mode, which has greatly improved the computational efficiency. Processing wound images acquired from UMASS Medical Center has demonstrated that the wound image analysis system provides accurate wounds area determination and color segmentation. For all wound images of size around 640 x 480, with complicated wound boundaries, the wound analysis consumed max 3s, which is 5 times faster than the same algorithm running on the CPU alone.
In this work we introduce two different techniques for the global optimization of surfaces and apply them to the task of
finding the optimal stitching seam between neighboring and overlapping 3D ultrasound volumes. Existing techniques
for US mosaicing, based on interpolation or planar seams, introduce artifacts into the composite volume especially when
using a large number of clinical scans. Our first method models the seam as a B-spline surface and treats its calculation
as a shape optimization problem. In this case the optimal location of the surface-defining control points is a large scale
constrained optimization problem, which is solved using a cooperatively coevolving particle swarm based approach. The
second method treats the seam selection as a voxel labeling problem, where each voxel in the composite volume is
labeled with its respective source volume. Therefore if we have N volumes, each voxel in the composite volume may be
assigned one of the N labels. The optimal labeling, which implicitly defines a seam, minimizes the intensity and gradient
difference between adjacent volumes The formulation is optimized using graphcuts, which guarantees that a global
minimum is achieved due to the submodularity of the energy function. The final composite volume is constructed voxel-wise
by taking the value of the source volume, which is designated by its label. Our application of this procedure is the
construction of composite ultrasound image volumes for incorporation into an ultrasound simulator. These methods are
validated on clinical US data acquired from obstetrics patients.
Ultrasound imaging is a noninvasive technique well-suited for detecting abnormalities like cysts, lesions and blood clots. In order to use 3D ultrasound to visualize the size and shape of such abnormalities, effective boundary detection methods are needed. A robust boundary detection technique using a nearest neighbor map (NNM) and applicable to multi-object cases has been developed. The algorithm contains three modules: pre-processor, main processor and boundary constructor. The pre-processor detects the object(s) and obtains geometrical as well as statistical information for each object, whereas the main processor uses that information to perform the final processing of the image. These first two modules perform image normalization, thresholding, filtering using median, wavelet, Wiener and morphological operation, estimation and boundary detection of object(s) using NNM, and calculation of object size and their location. The boundary constructor module implements an active contour model that uses information from previous modules to obtain seed-point(s). The algorithm has been found to offer high boundary detection accuracy of 96.4% for single scan plane (SSP) and 97.9 % for multiple scan plane (MSP) images. The algorithm was compared with Stick's algorithm and Gibbs Joint Probability Function based algorithm and was found to offer shorter execution time with higher accuracy than either of them. SSP numerically modeled ultrasound images, SSP real ultrasound images, MSP phantom images and MSP numerically modeled ultrasound images were processed.
Medical ultrasound images are noisy with speckle, acoustic noise and other artifacts. Reduction of speckle in particular is useful for CAD algorithms. We use two algorithms, namely, mean curvature evolution of the ultrasound image surface and a variation of the mean-curvature flow, to reduce speckle. The premise is that when we view the ultrasound image as a surface, the speckle appears as a high-curvature jagged layer over the true objects intensities and will reduce quickly on curvature evolution. We compare the two speckle reduction algorithms. We apply the speckle reduction to an image of a cyst and a 4-chamber view of the heart. We show significant, if not complete, speckle reduction, while keeping the relevant organ boundaries intact. On the speckle-reduced images, we apply a segmentation algorithm to detect objects. The segmentation algorithm is two-stepped. In the first step we choose a prior-shape and optimize the pose parameters to maximize the edge-pixels the curve falls into, using gradient ascent. In the second step, a radial motion is used to draw the contour points to the local-edges. We apply the algorithm on a cyst and obtain satisfactory results. We compare the total area inside the boundary output of our segmentation algorithm and to the total area covered by a hand-drawn boundary of the cyst, and the ratio is about 97%.
Studies suggest that the composition of atherosclerotic plaque in the carotid arteries is predictive of stroke risk. The goal of this investigation has been to explore how well the true integrated backscatter (IBS) from plaque regions can be measured <i>non-invasively</i> using ultrasound, based on which plaque composition may be inferred. To obtain the true arterial IBS non-invasively, the scattering and aberrating effect of the intervening tissue layers must be overcome. This is achieved by using the IBS from arterial blood as a reference backscatter, specifically the backscatter from a blood volume along the same scan line as and adjacent to the region of interest. The arterial blood IBS is obtained as an estimated mean of a stochastic process, after clutter removal. We have shown that the variance of the IBS estimate of the blood backscatter signal can be quantified and reduced to a tolerable level. The results are in the form of IBS profiles along the vessel. IBS profiles <i>not normalized with the IBS of the blood-mimicking fluid</i> have been measured for vessels phantom, with and without an intervening inhomogeneous medium; these results are contrasted with the corresponding <i>normalized</i> IBS profiles.