Unsharp masking technique using multiresolution analysis for computed radiography image enhancement

Ogoda, Makoto; Hishinuma, Kazuhiro; Yamada, Masahiko; Shimura, Kazuo

doi:10.1007/bf03168696

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…As shown in Figure 7 (b), the edge detection processing detects edges without regard to the magnitude of the isolated-point signal. However, the isolated-point detection processing shown in Figure 7 (c) detects the entire signal (2), but detects only the central low-density portion of signal (1). When the filter size for isolated-point detection processing is increased, signal (1) can be entirely extracted instead of detecting the leading end only.…”

Section: Enhancement Coefficient Signal Calculationsmentioning

confidence: 95%

Selective pattern enhancement processing for digital mammography, algorithms, and the visual evaluation

Yamada

Shimura

Nagata

2003

Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

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In order to enhance the micro calcifications selectively without enhancing noises, PEM (Pattern Enhancement Processing for Mammography) has been developed by utilizing not only the frequency information but also the structural information of the specified objects. PEM processing uses two structural characteristics i.e. steep edge structure and low-density isolated-point structure. The visual evaluation of PEM processing was done using two different resolution CR mammography images. The enhanced image by PEM processing was compared with the image without enhancement, and the conventional usharp-mask processed image. In the PEM processed image, an increase of noises due to enhancement was suppressed as compared with that in the conventional unsharp-mask processed image. The evaluation using CDMAM phantom showed that PEM processing improved the detection performance of a minute circular pattern. By combining PEM processing with the low and medium frequency enhancement processing, both mammary glands and micro calcifications are clearly enhanced.

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Section: Enhancement Coefficient Signal Calculationsmentioning

confidence: 95%

Selective pattern enhancement processing for digital mammography, algorithms, and the visual evaluation

Yamada

Shimura

Nagata

2003

Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

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“…This enhances edges and creates a perceived sharpness in the image, and can sometimes relate false features to the observer if applied too aggressively. Commercial algorithms such as MUSICA (Vuylsteke and Shoeters 1994;Andriole et al 1999) and MFP (Ogoda et al 1997) can implement changes over a broad range of contrast and edge enhancement. Image processing parameters require careful tuning on an exam-by-exam basis to ensure grayscale and spatial frequency enhancement are consistent according to radiologist preference.…”

Section: Image Processing Display and Protocols For Consistencymentioning

confidence: 99%

Digital Radiography: Image Quality and Radiation Dose

Seibert¹

2008

Health Physics

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Digital radiography devices, rapidly replacing analog screen-film detectors, are now common in diagnostic radiological imaging, where implementation has been accelerated by the commodity status of electronic imaging and display systems. The shift from narrow latitude, fixed-speed screen-film detectors to wide latitude, variable-speed digital detectors has created a flexible imaging system that can easily result in overexposures to the patient without the knowledge of the operator, thus potentially increasing the radiation burden of the patient population from radiographic examinations. In addition, image processing can be inappropriately applied causing inconsistent or artifactual appearance of anatomy, which can lead to misdiagnosis. On the other hand, many advantages can be obtained from the variable-speed digital detector, such as an ability to lower dose in many examinations, image post-processing for disease-specific conditions, display flexibility to change the appearance of the image and aid the physician in making a differential diagnosis, and easy access to digital images. An understanding of digital radiography is necessary to minimize the possibility of overexposures and inconsistent results, and to achieve the principle of as low as reasonably achievable (ALARA) for the safe and effective care of all patients. Thus many issues must be considered for optimal implementation of digital radiography, as reviewed in this article.

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“…Contrast enhancement has been used in mammography to make lesions more prominent (Lu et al, 1994;Li et al, 1997). Contrast enhancement can be used on digitized chest images (Rehm et al, 1990;Souto et al, 1992;McNitt-Gray et al, 1993) and routine bone images (Ogoda et al, 1997), or it could be used in radiation therapy to improve the contrast of port films.…”

Section: Contrast Enhancementmentioning

confidence: 99%

Applications of monotonic noise reduction algorithms in fMRI, phase estimation, and contrast enhancement

Weaver

1999

Int. J. Imaging Syst. Technol.

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Noise reduction using monotonic fits between extrema has been shown to work well on images, especially those with very low signal‐to‐noise ratios (SNRs). In this article we will explore three applications of monotonic noise reduction in magnetic resonance imaging (MRI). The first application is reducing noise in function MRI (fMRI) studies. Reduced noise allows greater flexibility. For example, it allows the activated regions to be identified using noisier images acquired in less time or fewer cycles of stimulation. Activation maps were calculated from the images after noise reduction had been applied to each image in the series. The parameters used in the noise reduction were optimized so the images produced best matched the average of the entire series. The CNR was improved significantly in the activation maps. The results can be extended to any other fMRI paradigm. The second application was reducing noise in complex data to improve the SNR of the phase in the complex MRI image. The error in the phase was reduced by a factor of three in the simulations shown. In the third application, we introduced a simple contrast‐enhancement method using monotonic noise reduction. To enhance contrast, the coarse features were reduced in size; the smaller size features were increased in size; very small features that are likely to be noise were attenuated. The result is a simple, effective method of improving the contrast of features of a selected size in images with no false features introduced. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 10, 177–185, 1999

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Unsharp masking technique using multiresolution analysis for computed radiography image enhancement

Cited by 15 publications

References 5 publications

Selective pattern enhancement processing for digital mammography, algorithms, and the visual evaluation

Selective pattern enhancement processing for digital mammography, algorithms, and the visual evaluation

Digital Radiography: Image Quality and Radiation Dose

Applications of monotonic noise reduction algorithms in fMRI, phase estimation, and contrast enhancement

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