Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet

Abstract

Mammography is an effective method for breast cancer detection and breast tumor analysis. In mammography, low dose x-ray is used for imaging, due to which the images are poor in contrast and are contaminated by noise. Hence it is difficult for the radiologist to screen the mammograms for diagnostic signs such as micro calcifications and masses. This ensures the need for image enhancement to aid radiologist. In this paper we present a different algorithm for enhancement of digital mammographic images. The proposed methodology uses mathematical morphology for contrast enhancement and wavelet for denoising. The main contribution of this paper is in differentiating the edge pixels from noise. A quantitative measure of Contrast Improvement Index (CII) and Edge Preservation Index (EPI) are used to evaluate the performance of the algorithm. The algorithm has been tested on a large number of images from standard dataset, comparing the results with the state-of-the-art. By both the analytical indices and ROC analysis, the proposed algorithm shows promising results in early detection of breast cancer and diagnosis.

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

“…The erosion of a gray-scale digital image I ( x , y ) by a structural element SE( i , j ) is defined as follows [7, 9, 11]. $\begin{array}{c}\left(I\otimes \text{SE}\right)\left(m,n\right)=\min \left\{I\left(m-i,n+j\right)-\text{SE}\left(i,j\right)\right\}.\end{array}$ …”

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology

“…The erosion of a gray-scale digital image ( , ) by a structural element SE( , ) is defined as follows [7,9,11].…”

“…As far as we know, most essential image processing algorithms applied the Gaussian filter, while the binomial filter applications are not popular in state-of-the-art image processing algorithms. Multiscale analysis technology based on the wavelet transform for mammogram contrast enhancement was applied in [2,7,[11][12][13][14][15]. However, the wavelet transform leads to undesirable artifacts in the enhanced image [16].…”

A Segmentation Method for Mammogram X-ray Image Based on Image Enhancement with Wavelet Fusion

“…The probability distribution of gray levels within the region of interest after GACE filter is estimated and a series of image partitions obtained according to the intensity proximity. Amutha [5] uses mathematical morphology and improved double orthogonal wavelet transform technology to enhance image of breast by distinguishing the edge pixels in noise area. Aydin [6] studied an enhancement image technology for breast microcalcifications images based on neural network technology and wavelet transform.…”