Total body photography for skin cancer screening Dengel, Lynn T.; Petroni, Gina R.; Judge, Joshua ...
International journal of dermatology,
November 2015, Volume:
54, Issue:
11
Journal Article
Peer reviewed
Open access
Background
Total body photography may aid in melanoma screening but is not widely applied due to time and cost. We hypothesized that a near‐simultaneous automated skin photo‐acquisition system would ...be acceptable to patients and could rapidly obtain total body images that enable visualization of pigmented skin lesions.
Methods
From February to May 2009, a study of 20 volunteers was performed at the University of Virginia to test a prototype 16‐camera imaging booth built by the research team and to guide development of special purpose software. For each participant, images were obtained before and after marking 10 lesions (five “easy” and five “difficult”), and images were evaluated to estimate visualization rates. Imaging logistical challenges were scored by the operator, and participant opinion was assessed by questionnaire.
Results
Average time for image capture was three minutes (range 2–5). All 55 “easy” lesions were visualized (sensitivity 100%, 90% CI 95–100%), and 54/55 “difficult” lesions were visualized (sensitivity 98%, 90% CI 92–100%). Operators and patients graded the imaging process favorably, with challenges identified regarding lighting and positioning.
Conclusions
Rapid‐acquisition automated skin photography is feasible with a low‐cost system, with excellent lesion visualization and participant acceptance. These data provide a basis for employing this method in clinical melanoma screening.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Inhaled hyperpolarized helium-3 (/sup 3/He) gas is a new magnetic resonance (MR) contrast agent that is being used to study lung functionality. To evaluate the total lung ventilation from the ...hyperpolarized /sup 3/He MR images, it is necessary to segment the lung cavities. This is difficult to accomplish using only the hyperpolarized /sup 3/He MR images, so traditional proton (/sup 1/H) MR images are frequently obtained concurrent with the hyperpolarized /sup 3/He MR examination. Segmentation of the lung cavities from traditional proton (/sup 1/H) MRI is a necessary first step in the analysis of hyperpolarized /sup 3/He MR images. In this paper, we develop an active contour model that provides a smooth boundary and accurately captures the high curvature features of the lung cavities from the /sup 1/H MR images. This segmentation method is the first parametric active contour model that facilitates straightforward merging of multiple contours. The proposed method of merging computes an external force field that is based on the solution of partial differential equations with boundary condition defined by the initial positions of the evolving contours. A theoretical connection with fluid flow in porous media and the proposed force field is established. Then by using the properties of fluid flow we prove that the proposed method indeed achieves merging and the contours stop at the object boundary as well. Experimental results involving merging in synthetic images are provided. The segmentation technique has been employed in lung /sup 1/H MR imaging for segmenting the total lung air space. This technology plays a key role in computing the functional air space from MR images that use hyperpolarized /sup 3/He gas as a contrast agent.
Abstract Cardiac hypertrophy is controlled by a complex signal transduction and gene regulatory network, containing multiple layers of crosstalk and feedback. While numerous individual components of ...this network have been identified, understanding how these elements are coordinated to regulate heart growth remains a challenge. Past approaches to measure cardiac myocyte hypertrophy have been manual and often qualitative, hindering the ability to systematically characterize the network's higher-order control structure and identify therapeutic targets. Here, we develop and validate an automated image analysis approach for objectively quantifying multiple hypertrophic phenotypes from immunofluorescence images. This approach incorporates cardiac myocyte-specific optimizations and provides quantitative measures of myocyte size, elongation, circularity, sarcomeric organization, and cell–cell contact. As a proof-of-concept, we examined the hypertrophic response to α-adrenergic, β-adrenergic, tumor necrosis factor (TNFα), insulin-like growth factor-1 (IGF-1), and fetal bovine serum pathways. While all five hypertrophic pathways increased myocyte size, other hypertrophic metrics were differentially regulated, forming a distinct phenotype signature for each pathway. Sarcomeric organization was uniquely enhanced by α-adrenergic signaling. TNFα and α-adrenergic pathways markedly decreased cell circularity due to increased myocyte protrusion. Surprisingly, adrenergic and IGF-1 pathways differentially regulated myocyte–myocyte contact, potentially forming a feed-forward loop that regulates hypertrophy. Automated image analysis unlocks a range of new quantitative phenotypic data, aiding dissection of the complex hypertrophic signaling network and enabling myocyte-based high-content drug screening.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in ...vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.
Tracking the movement of rolling leukocytes in vivo contributes to the understanding of the mechanism of the inflammatory process and to the development of anti-inflammatory drugs. Several roadblocks ...exist that hinder successful automated tracking including the moving background, the severe image noise and clutter, the occlusion of the target leukocyte by other leukocytes and structures, the jitter caused by the breathing movement of the living animal, and the weak image contrast. In this paper, a Monte Carlo tracker is developed for automatically tracking a single rolling leukocyte in vivo. Based on the leukocyte movement information and the image intensity features, a specialized sample-weighting criterion is tailored to the application. In comparison with a snake-based tracker, our experiments show that, as the noise intensity level increases, the performance of the snake tracker degrades more than that of the Monte Carlo tracker. In cases, where the leukocyte is observed in contact with the vessel wall, the Monte Carlo tracker is less affected by the image clutter. From tracking within 99 intravital microscopic video sequences, the Monte Carlo tracker exhibits superior performance in the reduced localization error and the increased number of frames tracked when compared with the centroid tracker, the correlation tracker and the GVF snake tracker.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Abstract Artifacts due to enhancement, reverberation, and multi-path reflection are commonly encountered in medical ultrasound imaging. These artifacts can adversely affect an automated image ...quantification algorithm or interfere with a physician’s assessment of a radiological image. This paper proposes a soft wavelet thresholding method to replace regions adversely affected by these artifacts with the texture due to the underlying tissue(s), which were originally obscured. Our proposed method soft thresholds the wavelet coefficients of affected regions to estimate the reflectivity values caused by these artifacts. By subtracting the estimated reflectivity values of the artifacts from the original reflectivity values, estimates of artifact reduced reflectivity values are attained. The improvements of our proposed method are substantiated by an evaluation of Field II simulated, in vivo mouse and human heart B mode images.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Event detection in sparse, non-uniformly sampled multidimensional data eludes traditional signal processing techniques. We propose a method that, within the graph-theoretic framework, successfully ...addresses the task of localizing and characterizing active regions by leveraging the coherent behavior detection capabilities of the covariance matrix together with the natural definition of graph neighborhood connectivity provided by the Laplacian. We further extend this framework using scale space formulation to detect the extent of active region. Our method exhibits significant improvement in detecting active regions compared to other similar state-of-the art techniques. The method is demonstrated on interferometric synthetic aperture radar (InSAR) data that is both sparse and non-uniformly sampled. Comparison with three widely used event detection methods reveals the efficacy and efficiency of the Laplacian-weighted covariance technique.
In visual object tracking, robust and accurate scale estimation of a target is a challenging task. Despite the associated computational expense, existing tracking methods cannot accommodate large ...scale variations. Here, we propose a scale searching scheme that obtains robust and accurate scale estimation by incorporating a novel and robust criterion, the average peak-to-correlation energy, into a multi-resolution translation filter framework. To address the problem of computational expense, we introduce an expeditious search strategy. The resulting system is named FAST: Fast and Accurate Scale estimation for Tracking. Comprehensive evaluation using the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework can accommodate large scale variation while also yielding computational efficiency.
Radar images are often collected over the same region over time. To provide smoothing of such imagery without effacing temporal changes in the scene, we put forth an anisotropic diffusion technique. ...Traditionally, with synthetic aperture radar images, the mean of time series is utilized to produce a single despeckled image that discards temporal information. In contrast, we propose a statistical approach designed to reduce speckle noise in each image. The new filter incorporates temporal information essential to detection of potential change events for transportation infrastructure. Results demonstrate the efficacy of the approach, showing lower mean squared error than leading methods.
We explore the application of area morphology to image classification. From the input image, a scale space is created by successive application of an area morphology operator. The pixels within the ...scale space corresponding to the same image location form a scale space vector. A scale space vector therefore contains the intensity of a particular pixel for a given set of scales, determined in this approach by image granulometry. Using the standard k-means algorithm or the fuzzy c-means algorithm, the image pixels can be classified by clustering the associated scale space vectors. The scale space classifier presented here is rooted in the novel area open-close and area close-open scale spaces. Unlike other scale generating filters, the area operators affect the image by removing connected components within the image level sets that do not satisfy the minimum area criterion. To show that the area open-close and area close-open scale spaces provide an effective multiscale structure for image classification, we demonstrate the fidelity, causality, and edge localization properties for the scale spaces. The analysis also reveals that the area open-close and area close-open scale spaces improve classification by clustering members of similar objects more effectively than the fixed scale classifier. Experimental results are provided that demonstrate the reduction in intra-region classification error and in overall classification error given by the scale space classifier for classification applications where object scale is important. In both visual and objective comparisons, the scale space approach outperforms the traditional fixed scale clustering algorithms and the parametric Bayesian classifier for classification tasks that depend on object scale.
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