An image visualization tool in mammography

Sakellaropoulos, P.; Costaridou, Lena; Panayiotakis, G.

doi:10.1080/146392399298528

Cited by 39 publications

(8 citation statements)

References 19 publications

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“…The Monte Carlo generated images were visualized and analyzed using a software (Medical Image Visualization) developed for medical image processing and analysis applications (Sakellaropoulos et al 1999).…”

Section: Mathematical Breast Phantom and Calculation Of The Image Cha...mentioning

confidence: 99%

Contrast-to-noise ratio in magnification mammography: a Monte Carlo study

Koutalonis¹,

Delis²,

Spyrou³

et al. 2007

Phys. Med. Biol.

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Magnification views are a common way to perform a secondary examination when suspicious abnormalities are found in a screening mammogram. The visibility of microcalcifications and breast lesions is restricted by the compromise between the image quality and the absorbed dose. In this study, image quality characteristics in magnification mammography were evaluated based on Monte Carlo techniques. A breast phantom was utilized, simulating a homogeneous mixture of adipose and glandular tissue in various percentages of glandularity, containing inhomogeneities of various sizes and compositions. The effect of the magnification degree, breast glandularity, tube voltage and anode/filter material combination on image quality characteristics was investigated in terms of a contrast-to-noise ratio (CNR). A performance index PI(nu) was introduced in order to study the overall performance of various anode/filter combinations under different exposure parameters. Results demonstrate that CNR is improved with the degree of magnification and degraded as the breast glandularity is increased. Degree of magnification 1.3 offers the best overall performance for most of the anode/filter combinations utilized. Under magnification conditions, the role of dose is demoted against the image quality, as magnification views are secondary, diagnostic examinations and not screening procedures oriented to non-symptomatic women. For decreased image quality weighting, some anode/filter combinations different from Mo/0.030 mmMo can be utilized as they offer a similar performance index. However, if the desired weighting for the image quality is high, the Mo/0.030 mmMo combination has the best overall performance.

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Section: Mathematical Breast Phantom and Calculation Of The Image Cha...mentioning

confidence: 99%

Contrast-to-noise ratio in magnification mammography: a Monte Carlo study

Koutalonis¹,

Delis²,

Spyrou³

et al. 2007

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…Another approach uses a multiscale edge representation, provided by the same type of wavelet transform, in order to accentuate multiscale edges (Lu and Heally et al 1994). In this paper a new method is presented, continuing our previous efforts (Sakellaropoulos et al 1999(Sakellaropoulos et al , 2000 for contrast enhancement and soft-copy reading of mammograms. The aim is to achieve optimized presentation of mammographic image contrast on digital displays.…”

Section: Introductionmentioning

confidence: 99%

A wavelet-based spatially adaptive method for mammographic contrast enhancement

Costaridou²,

2003

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A method aimed at minimizing image noise while optimizing contrast of image features is presented. The method is generic and it is based on local modification of multiscale gradient magnitude values provided by the redundant dyadic wavelet transform. Denoising is accomplished by a spatially adaptive thresholding strategy, taking into account local signal and noise standard deviation. Noise standard deviation is estimated from the background of the mammogram. Contrast enhancement is accomplished by applying a local linear mapping operator on denoised wavelet magnitude values. The operator normalizes local gradient magnitude maxima to the global maximum of the first scale magnitude subimage. Coefficient mapping is controlled by a local gain limit parameter. The processed image is derived by reconstruction from the modified wavelet coefficients. The method is demonstrated with a simulated image with added Gaussian noise, while an initial quantitative performance evaluation using 22 images from the DDSM database was performed. Enhancement was applied globally to each mammogram, using the same local gain limit value. Quantitative contrast and noise metrics were used to evaluate the quality of processed image regions containing verified lesions. Results suggest that the method offers significantly improved performance over conventional and previously reported global wavelet contrast enhancement methods. The average contrast improvement, noise amplification and contrast-to-noise ratio improvement indices were measured as 9.04, 4.86 and 3.04, respectively. In addition, in a pilot preference study, the proposed method demonstrated the highest ranking, among the methods compared. The method was implemented in C++ and integrated into a medical image visualization tool.

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“…A case sample of 50 cervical intervertebral discs (25 normal and 25 degenerated), which represent the Regions of Interest (ROIs) within the images, was analyzed. These disc-ROIs were determined by manual delineation, utilizing dedicated digital image processing software [8] (figure 2).…”

Section: Mri Datamentioning

confidence: 99%

Computer assisted characterization of cervical intervertebral disc degeneration in MRI

et al. 2009

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A texture-based pattern recognition system is proposed for the automatic characterization of cervical intervertebral disc degeneration from saggital magnetic resonance images of the spine. A case sample of 50 manually segmented ROIs, corresponding to 25 normal and 25 degenerated discs, was analyzed and textural features were generated from each disc-ROI. Student's t-test verified the existence of statistically significant differences between textural feature values generated from normal and degenerated discs. This finding is indicative of disc image texture differentiation due to the degeneration of the disc. The generated features were employed in the design of a pattern recognition system based on the Least Squares Minimum Distance classifier. The system achieved a classification accuracy of 94% and it may be of value to physicians for the assessment of cervical intervertebral disc degeneration in MRI.

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An image visualization tool in mammography

Cited by 39 publications

References 19 publications

Contrast-to-noise ratio in magnification mammography: a Monte Carlo study

Contrast-to-noise ratio in magnification mammography: a Monte Carlo study

A wavelet-based spatially adaptive method for mammographic contrast enhancement

Computer assisted characterization of cervical intervertebral disc degeneration in MRI

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