Nonlinear Unsharp Masking for Mammogram Enhancement

Panetta, Karen; Zhou, Yicong; Agaian, Sos S.; Jia, Hongwei

doi:10.1109/titb.2011.2164259

Cited by 181 publications

(76 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Malignant [1] Asymmetry between breasts and some characteristic lesions like micro-calcifications, masses and architectural distortions are indicator of breast cancer. Micro-calcifications are small in size thus hard to detect.…”

Section: Fig 1: Stages Of Cancer From Normal To Benign Andmentioning

confidence: 99%

Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms

Pandey¹,

Singh²,

Singh³

2013

IJCA

View full text Add to dashboard Cite

The most common cancer of women is breast cancer which is the leading cause of cancer-related death among women aged 15 to 54. The risk of cancer increases after the age of 40's. Thus earlier detection of breast cancer increases the probability of survival of the patient. For its detection mammography is done, but many of the masses remain either undetected or falsely detected due to poor contrast and noise present in mammographic images. Thus for earlier detection of cancerous masses many enhancement techniques are applied. In this paper various set of performance metrics that measure the quality of the image enhancement of mammographic images in a CAD framework that automatically finds masses using machine learning techniques. These performance metrics quantitatively measures the best suited image enhancement on a per mammogram basis, which improves the quality of ensuing image segmentation much better than using the same enhancement method for all mammograms.

show abstract

Section: Fig 1: Stages Of Cancer From Normal To Benign Andmentioning

confidence: 99%

Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms

Pandey¹,

Singh²,

Singh³

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…Second-Derivative-like Measure of Enhancement (SDME) is a visibility operator [19] and a metric for quantitatively assessing image quality [20][21]. This visibility operator can be viewed as a second derivative analogue of the Michelson contrast measure.…”

Section: Second Derivative-like Measure Of Enhancementmentioning

confidence: 99%

“…So the method is proposed in this paper, which uses SDME measure to decide the optimal stopping point. SDME measure is properly correlated with the noise level and intensity contrast (which indicates the "visibility" [19][20][21]) of the structured regions of an image. SDME measure is modified such that its value drops if the variance of noise rises or if the blur increases in the image.…”

Section: Introductionmentioning

confidence: 99%

SDME Quality Measure based Stopping Criteria for Iterative Deblurring Algorithms

Shah¹,

Dalal²

2016

ijacsa

View full text Add to dashboard Cite

Abstract-Deblurring from motion problem with or without noise is ill-posed inverse problem and almost all inverse problem require some sort of parameter selection. Quality of restored image in iterative motion deblurring is dependent on optimal stopping point or regularization parameter selection. At optimal point reconstructed image is best matched to original image and for other points either data mismatch occurs and over smoothing is resulted. The methods used for optimal parameter selection are formulated based on correct estimation of noise variance or with restrictive assumption on noise. Some methods involved heavy computation and produce delay in final output. In this paper we propose the method which calculate visual image quality of reconstructed image with the help of Second derivative like measure of enhancement (SDME) and helps to efficiently decide optimal stopping condition which has been checked for leading image deblurring algorithm.

show abstract

“…where X = For better image quality assessment, a Second Derivative like MEasurement (SDME) [33], [19] was introduced and this measure is shown to have better performance than other measures in evaluating the image visual quality after enhancement.…”

Section: Blind-reference Iqa Metricsmentioning

confidence: 99%

IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements

L¹,

Gopikakumari²

2013

IJCA

View full text Add to dashboard Cite

Evaluation of images, after processing, is an important step for determining how well the images are being processed. Quality of image is usually assessed using image quality metrics. Unfortunately, most of the commonly used metrics cannot adequately describe the visual quality of the enhanced image. There is no universal measure, which specifies both the objective and subjective validity of the enhancement for all types of images. This paper is a study of the various quantitative metrics for enhancement against changes in contrast and sharpness of both general and medical images. A new metric is proposed that is useful for measuring the improvement in contrast as well as sharpness. It is computationally simple and can be used for all types of images.

show abstract

Nonlinear Unsharp Masking for Mammogram Enhancement

Cited by 181 publications

References 52 publications

Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms

Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms

SDME Quality Measure based Stopping Criteria for Iterative Deblurring Algorithms

IEM: A New Image Enhancement Metric for Contrast and Sharpness Measurements

Contact Info

Product

Resources

About