Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms

Abstract

Mammograms are difficult to interpret, especially of cancers at their early stages. In this paper, we analyze the effectiveness of our adaptive neighborhood contrast enhancement (ANCE) technique in increasing the sensitivity of breast cancer diagnosis. Seventy-eight screen-film mammograms of 21 difficult cases (14 benign and seven malignant), 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a high-resolution of about 4096 x 2048 x 10-bit pixels and then processed with the ANCE method. Unprocessed and processed digitized mammograms as well as the original films were presented to six experienced radiologists for a receiver operating characteristic (ROC) evaluation for the difficult case set and to three reference radiologists for the interval cancer set. The results show that the radiologists' performance with the ANCE-processed images is the best among the three sets of images (original, digitized, and enhanced) in terms of area under the ROC curve and that diagnostic sensitivity is improved by the ANCE algorithm. All of the 19 interval cancer cases not detected with the original films of earlier mammographic examinations were diagnosed as malignant with the corresponding ANCE-processed versions, while only one of the six benign cases initially labeled correctly with the original mammograms was interpreted as malignant after enhancement. McNemar's tests of symmetry indicated that the diagnostic confidence for the interval cancer cases was improved by the ANCE procedure with a high level of statistical significance (p-values of 0.0001-0.005) and with no significant effect on the diagnosis of the benign control cases (p-values of 0.08-0.1). This study demonstrates the potential for improvement of diagnostic performance in early detection of breast cancer with digital image enhancement.

A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs

“…Rangayyan et al [30,38] investigated the performance of their ANCE algorithm in increasing the sensitivity of breast cancer diagnosis by using ROC analysis and McNemar's tests [28]. A set of 78 screen-film mammograms of 21 difficult cases (14 malignant and seven benign) and another set of 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a resolution of about 4096 Â 2048 Â 10-bit pixels, and subsequently processed with the ANCE algorithm.…”

“…The enhancement of mammographic images could improve the accuracy of detection of early signs of breast cancer. For reviews on image enhancement in mammography, see Rangayyan [28], Morrow et al [29], and Rangayyan et al [30].…”

Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection

“…The objective of this procedure is to increase the contrast between mammogram structures and their background, while providing a relatively uniform intensity to all of the structures. Previous studies [17,21,23,26] have demonstrated the benefits of applying a contrast enhancement processing driven by the local characteristics of the mammograms. In our contrast enhancement routine, statistical measures of the pixel intensities in local neighborhoods are employed to automatically set the parameters of the transform applied to each pixel.…”

Filtering noise on mammographic phantom images using local contrast modification functions

“…While the latter group enhance the contrast without measuring the contrast itself, the former one use a local definition of the contrast information which is then modified by a mapping function to generate the enhanced image. Several studies have been devoted to contrast enhancement on mammographic images [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] using both direct and indirect enhancement methods. Techniques used for that purpose reveal six broad categories: unsharp masking [1,2] histogram equalization [3,4] region based or adaptive neighbourhood contrast enhancement [5][6][7][8][9] wavelet enhancement [10,11], fuzzy modelling [12,13] and spatial filtering [14,15].…”