Breast Component Adaptive Wavelet Enhancement for Soft-Copy Display of Mammograms

Skiadopoulos, Spyros; Karahaliou, Anna; Sakellaropoulos, Filippos; Panayiotakis, George; Costaridou, Lena

doi:10.1007/11783237_74

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…For pre-processing mammogram images many techniques are used for enhancing the contrast in digital mammograms. Generally, the digital mammograms are broken down into multiscale subband representation using the Contourlet transform [5] or different types of wavelet transforms [6][7][8][9][10][11]. After that, the transform coefficients in each subband of the multiscale representation are modified using different techniques, such as nonlinear filtering [12], regression-based extrapolation [13], adaptive unsharp masking [14], the wavelet shrinkage function [15], or direct contrast modification [16].…”

Section: Introductionmentioning

confidence: 99%

Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network

2015

J Inf Process Syst

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Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and graylevel co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

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Section: Introductionmentioning

confidence: 99%

Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network

2015

J Inf Process Syst

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“…Many enhancement algorithms for mammograms have been developed by utilizing different image enhancement technologies such as wavelet enhancement [4,5] in the frequency domain, and contrast limited adaptive histogram equalization (CLAHE) [6] as well as adaptive neighborhood contrast enhancement (ANCE) [3] in the spatial domain. These methods have their own limitations such as over-enhancement of brightness, or presence of noises orland artifacts.…”

Section: Introductionmentioning

confidence: 99%

Human visual system based mammogram enhancement and analysis

Zhou

Panetta

Agaian

2010

2010 2nd International Conference on Image Processing Theory, Tools and Applications

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This paper introduces a new mammogram enhancement algorithm using the human visual system (HVS) based image decomposition. A new enhancement measure based on the second derivative is also introduced to measure and assess the enhancement performance. Experimental results show that the presented algorithm can improve the visual quality of fine details in mammograms. The HVS-based image decomposition can segment the regions/objects from their surroundings. It offers the users flexibility to enhance either sub-images containing only significant illumination information or all the sub-images of the original mammograms. The algorithm can be used in the computer aided diagnosis systems for breast cancer detection. K�words-Mammogram enhancement, HVS-based image decomposition, nonlinear filtering, breast cancer detection.

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An Efficient Method for Contrast Enhancement of Digital Mammographic Images

Kumar

Chandra

2014

Soft Computing Techniques in Engineering Applications

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Breast Component Adaptive Wavelet Enhancement for Soft-Copy Display of Mammograms

Cited by 4 publications

References 14 publications

Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network

Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network

Human visual system based mammogram enhancement and analysis

An Efficient Method for Contrast Enhancement of Digital Mammographic Images

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