Distance-based functions for image comparison

Gesù, Vito Di; Старовойтов, В. В.

doi:10.1016/s0167-8655(98)00115-9

Cited by 100 publications

(52 citation statements)

References 17 publications

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“…This metric is useful for comparing an image to a reference image in the same type of way that the human eye perceives image differences: by examining the relationships between the pixels that are spatially near to each other. This is opposed to other popular methods, such as mean-square error (MSE) or cross-correlation [19,20], which compare intensities of pixels solely on a pixel-to-pixel basis. In particular, the SSIM takes into consideration three distinct image qualities: the luminance, the contrast, and the structure.…”

Section: Scans With Healthy Volunteersmentioning

confidence: 99%

Time-Domain Microwave Radar Applied to Breast Imaging: Measurement Reliability in a Clinical Setting

Porter¹,

Santorelli²,

Popović³

2014

PIER

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Abstract-This work presents an evaluation of the measurement challenges in clinical testing of our microwave breast cancer screening system. The time-domain radar system contains a multistatic 16-antenna hemi-spherical array operating in the 2-4 GHz frequency range. We investigate, for the first time with such a system in clinical trials, the repeatability of measurements and its effect on image reconstruction. We record vertical and horizontal measurement uncertainties under different scenarios and verify using previously introduced compensation methods that they can be successfully reduced to an acceptable level from the standpoint of image reconstruction. We also examine how placement of an immersion medium can affect collected breast scan data. Finally, we probe the repeatability and consistency of measurements with patients. With the goal of confirming the feasibility of frequent breast health monitoring, with our system, we obtain a total of 342 breast scans collected over 57 patient visits to determine how much scan data varies when there are no changes in between scans, and how much it varies when the patient is repositioned in the system. We confirm that, by taking care in patient positioning in the system and with respect to the immersion medium, the measurement repeatability is high.

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Section: Scans With Healthy Volunteersmentioning

confidence: 99%

Time-Domain Microwave Radar Applied to Breast Imaging: Measurement Reliability in a Clinical Setting

Porter¹,

Santorelli²,

Popović³

2014

PIER

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“…Numerous measures have been proposed that can be used to express the difference (or similarity) in shape between two surfaces; most notably in the area of image retrieval [7][8][9] and shape retrieval [10,11]. However, most of these measures, such as the commonly used Hausdorff distance, only express similarity at a global level.…”

Section: Comparative Visualization Of 3d Surfacesmentioning

confidence: 99%

Image-based rendering of intersecting surfaces for dynamic comparative visualization

et al. 2010

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Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503-510, 2005). In this paper we present an imagebased formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading.We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regard- ing the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface's distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface.Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models.

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“…We present two adaptations of the Hausdorff distance (AHD, AHD2) based on its original and most common definition [16,18]:…”

Section: Neighborhood-based Measuresmentioning

confidence: 99%

“…The Averaged Distance (AVG) is an image comparison measure introduced by [16]. We modified it to work with missing values as we have done for the adapted Hausdorff distance.…”

Section: Averaged Distance (Avg)mentioning

confidence: 99%

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Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images

2010

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Satellite observations of the oceans have great potential to improve the quality and predictive power of numerical ocean models and are frequently used in model skill assessment as well as data assimilation. In this study we introduce and compare various measures for the quantitative comparison of satellite images and model output that have not been used in this context before. We devised a series of test to compare their performance, including their sensitivity to noise and missing values, which are ubiquitous in satellite images. Our results show that two of our adapted measures, the Adapted Gray Block distance and the entropic distance D2, perform better than the commonly used root mean square error and image correlation.

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Distance-based functions for image comparison

Cited by 100 publications

References 17 publications

Time-Domain Microwave Radar Applied to Breast Imaging: Measurement Reliability in a Clinical Setting

Time-Domain Microwave Radar Applied to Breast Imaging: Measurement Reliability in a Clinical Setting

Image-based rendering of intersecting surfaces for dynamic comparative visualization

Introduction and Assessment of Measures for Quantitative Model-Data Comparison Using Satellite Images

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