Entropy maximisation histogram modification scheme for image enhancement

Breast cancer in women has been the most often diagnosed cancer. Digital mammogram becomes the most effective imaging method to detect breast cancer in early stage. In breast cancer screening, radiologists typically miss about 10-30% of tumors due to the speculated margins of lesions. Mammogram is a low contrast image whose quality needs to be improved for better interpretation. The performance of the validation of pre-processing methods for mammogram image enhancement is done by performance metrics such as peak signal to noise ratio (PSNR) and mean square error (MSE). Good filtering technique having higher PSNR and low MSE value. Experimental results on the digital database for screening mammography images shown that the non-local mean filter is better for mammogram image enhancement. Further we proposed mammogram images enhancement by entropy improvement method by considering nonlocal filtered images. These methodologies could add to the effective discovery of masses and micro calcifications in mammograms.

show abstract

“…with histogram equalization [21,22] on the mammogram image data set. Then the morphological operation has been carried out for image smoothing.…”

Section: Proposed Approach For Entropy Improvementmentioning

confidence: 99%

Enhancement of mammogram images by using entropy improvement approach

Reddy

Kumar

2019

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…HSQHE preserves image brightness more accurately in less time interval. Zhao Wei [13] proposed a EMHM (Entropy maximization histogram modification) method, which consists of dividing the global histogram equalization into two steps, pixel populations mergence (PPM) step which goes in hand with entropy maximisation rule followed by the grey-levels distribution (GLD). Proposed method performs better than the existing methods.…”

Section: Literature Surveymentioning

confidence: 99%

Image contrast enhancement techniques-a survey

Padmavathy¹,

Priya²

2018

IJET

View full text Add to dashboard Cite

Image Enhancement plays an essential role in a wide area of vision applications. Image enhancement is a technique used to enhance the qual-ity of the image such that it can be easily viewed by both men and machine.Contrast makes a visual difference that makes an object distin-guishable from background and other objects. The major goal of image contrast enhancement is to increase the visual quality of the image. In this research study, various image contrast enhancement techniques are reviewed. This research work also focuses on the comparative study of contrast enhancement techniques for identifying an effective contrast enhancement technique.

show abstract

“…The entropy value can also be considered as the averaged amount of information required to encode the image's pixels values and may help in reservation of memory required to further manipulate an image. Entropy of an image has also been used for the enhancement of an image as indicated by Wei et al [2]. The entropy of an image can be estimated using equation 2 below:…”

Section: A Image Brightness and Entropymentioning

confidence: 99%

Fast Generation of Image’s Histogram Using Approximation Technique for Image Processing Algorithms

Appiah¹,

Hayfron-Acquah²

2018

IJIGSP

View full text Add to dashboard Cite

The process of generating histogram from a given image is a common practice in the image processing domain. Statistical information that is generated using histograms enables various algorithms to perform a lot of pre-processing task within the field of image processing and computer vision. The statistical subtasks of most algorithms are normally effectively computed when the histogram of the image is known. Information such as mean, median, mode, variance, standard deviation, etc. can easily be computed when the histogram of a given dataset is provided. Image brightness, entropy, contrast enhancement, threshold value estimation and image compression models or algorithms employ histogram to get the work done successfully. The challenge with the generation of the histogram is that, as the size of the image increases, the time expected to traverse all elements in the image also increases. This results in high computational time complexity for algorithms that employs the generation histogram as subtask. Generally the time complexity of histogram algorithms can be estimated as O(N 2) where the height of the image and its width are almost the same. This paper proposes an approximated method for the generation of the histogram that can reduce significantly the time expected to complete a histogram generation and also produce histograms that are acceptable for further processing. The method can theoretically reduce the computational time to a fraction of the time expected by the actual method and still generate outputs of acceptable level for algorithms such as Histogram Equalization (HE) for contrast enhancement and Otsu automatic threshold estimation.

show abstract

Entropy maximisation histogram modification scheme for image enhancement

Cited by 30 publications

References 11 publications

Enhancement of mammogram images by using entropy improvement approach

Enhancement of mammogram images by using entropy improvement approach

Image contrast enhancement techniques-a survey

Fast Generation of Image’s Histogram Using Approximation Technique for Image Processing Algorithms

Contact Info

Product

Resources

About