Enhancement of chest and breast radiographs by automatic spatial filtering

Tahoces, Pablo G.; Correa, J. L. del Río; Souto, Miguel Abel; Gonzalez, Carlos F.; Gomez, L.; Vidal, José Martı́nez

doi:10.1109/42.97582

Cited by 78 publications

(31 citation statements)

References 21 publications

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“…The characteristic shape of the inverted lognormal distribution is used to properly describe the asymmetric shape of the bone peak. This particular distribution has different formulations: the probability density of the one used here is given by (8) and it is defined only for . Its mean and variance are, respectively (9) (10) The other components of the mixture model make up a Gaussian distribution, described by (11) where and are the mean and the variance of the distribution.…”

Section: Mixture Model For Histogram Based Clusteringmentioning

confidence: 99%

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Enhancing digital cephalic radiography with mixture models and local gamma correction

Frosio

Ferrigno²,

Borghese³

2006

IEEE Trans. Med. Imaging

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Abstract-We present a new algorithm, called the soft-tissue filter, that can make both soft and bone tissue clearly visible in digital cephalic radiographies under a wide range of exposures. It uses a mixture model made up of two Gaussian distributions and one inverted lognormal distribution to analyze the image histogram. The image is clustered in three parts: background, soft tissue, and bone using this model. Improvement in the visibility of both structures is achieved through a local transformation based on gamma correction, stretching, and saturation, which is applied using different parameters for bone and soft-tissue pixels. A processing time of 1 s for 5 Mpixel images allows the filter to operate in real time. Although the default value of the filter parameters is adequate for most images, real-time operation allows adjustment to recover under-and overexposed images or to obtain the best quality subjectively. The filter was extensively clinically tested: quantitative and qualitative results are reported here.

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Section: Mixture Model For Histogram Based Clusteringmentioning

confidence: 99%

“…Unsharp masking (UM) is one of the most widely used techniques, [4]- [8]. It can be implemented to work in real time, but it enhances only the small features of the image and increases the noise.…”

mentioning

confidence: 99%

Enhancing digital cephalic radiography with mixture models and local gamma correction

Frosio

Ferrigno²,

Borghese³

2006

IEEE Trans. Med. Imaging

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“…Notice that since the DC-Cap contains much of the energy distribution, and is usually not modified during the enhancement procedure [12]. As shown above, the enhancement function can be implemented independently of a particular set of filters and easily incorporated into a filter bank to provide the benefits of multi-scale enhancement [13,14].…”

Section: Contrast Enhancement Using Over-complete Dyadic Spline Waveletsmentioning

confidence: 99%

<title>Contrast enhancement by multiscale adaptive histogram equalization</title>

2001

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An approach for contrast enhancement utilizing multi-scale analysis is introduced. Sub-band coefficients were modified by the method of adaptive histogram equalization. To achieve optimal contrast enhancement, the sizes of sub-regions were chosen with consideration to the support of the analysis filters. The enhanced images provided subtle details of tissues that are only visible with tedious contrast/brightness windowing methods currently used in clinical reading. We present results on chest CT data, which shows significant improvement over existing state-of-the-art methods: unsharp masking, adaptive histogram equalization (AHE), and the contrast limited adaptive histogram equalization (CLAHE). A systematic study on 109 clinical chest CT images by three radiologists suggests the promise of this method in terms of both interpretation time and diagnostic performance on different pathological cases. In addition, radiologists observed no noticeable artifacts or amplification of noise that usually appears in traditional adaptive histogram equalization and its variations.

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“…It is often done before the image is to be analyzed [2]. De-noising is mainly used to remove noise that is present and retains the significant information, regardless of the frequency contents of the signal [3]. During the process of de-noising, much attention is kept on how well the edges are preserved and how much of noise granularity been removed [4].…”

Section: Introductionmentioning

confidence: 99%

“…The rapid advancement in the field of digital image processing has allowed to enhancement, filtering, or any other types of digital image manipulation that may improve diagnostic interpretation [3], [4]. Digital image format is required in computer-aided diagnosis (CAD) schemes to assist the radiologist in the detection of radiological features that could point to different pathologies [12], [13].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach to De-noising Mammogram Images

Rajurkar¹,

Singnapure²

2014

International Journal of Advanced Research in Computer and Comm

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Abstract:The noisy nature of digital mammograms and low contrast of doubtful areas which make medical images denoising a challenging problem. In the field of image processing, different types of denoising algorithm are used to remove the unnecessary noise such as additive white Gaussian noise, salt and pepper noise, impulsive noise, as well used to smoothen the image. In the existing work, a novel gradient histogram preservation (GHP) algorithm was developed to improve the texture which exhibits the reduced quality in recognition of human image experiments. To remove the noise, Discrete Wavelet Transform (DWT) algorithm is used to eliminate the noisiness present in the medical image to preserve the texture structures. Proposed approach will efficiently calculate peak signal noise ratio (PSNR) parameters and make the enhanced image without noise and obtains the quality of image without any loss of data.

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Enhancement of chest and breast radiographs by automatic spatial filtering

Cited by 78 publications

References 21 publications

Enhancing digital cephalic radiography with mixture models and local gamma correction

Enhancing digital cephalic radiography with mixture models and local gamma correction

<title>Contrast enhancement by multiscale adaptive histogram equalization</title>

An Efficient Approach to De-noising Mammogram Images

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