Evaluating the effect of a wavelet enhancement method in characterization of simulated lesions embedded in dense breast parenchyma

Abstract

Presence of dense parenchyma in mammographic images masks lesions resulting in either missed detections or mischaracterizations, thus decreasing mammographic sensitivity and specificity. The aim of this study is evaluating the effect of a wavelet enhancement method on dense parenchyma for a lesion contour characterization task, using simulated lesions. The method is recently introduced, based on a two-stage process, locally adaptive denoising by soft-thresholding and enhancement by linear stretching. Sixty simulated low-contrast lesions of known image characteristics were generated and embedded in dense breast areas of normal mammographic images selected from the DDSM database. Evaluation was carried out by an observer performance comparative study between the processed and initial images. The task for four radiologists was to classify each simulated lesion with respect to contour sharpness/unsharpness. ROC analysis was performed. Combining radiologists' responses, values of the area under ROC curve (Az) were 0.93 (95% CI 0.89, 0.96) and 0.81 (CI 0.75, 0.86) for processed and initial images, respectively. This difference in Az values was statistically significant (Student's t-test, P<0.05), indicating the effectiveness of the enhancement method. The specific wavelet enhancement method should be tested for lesion contour characterization tasks in softcopy-based mammographic display environment using naturally occurring pathological lesions and normal cases.

Can electronic zoom replace magnification in mammography? A comparative Monte Carlo study

“…Among the new techniques introduced in the effort to replace magnification views, image post-processing, often facilitated by digital mammography, has become very popular [11][12][13]. Electronic magnification (zoom) of digital (or digitised) screening mammograms has recently come to the foreground of this research area and many authors are addressing this alternative.…”

Figure of Image Quality and Information Capacity in Digital Mammography

“…Therefore, the radiologist should be well aware of the interpretation of post processed images. In current literature the influence of post processing dedicated software on resolution and contrast, has been investigated [6] in terms of subjective measurements via human observers [9, 10] as well as objective calculations [11, 12]. Additionally, overall image acquisition characterization has been studied through the introduction of various indexes such as the Figure of Merit (FOM) in both mammography and general radiography [11, 12] and the Global Figure of Merit (GFM) in chest radiography [13] as well as the Image Quality Indicator (IQI) in industrial radiography [14].…”

Investigating the dependence of breast calcifications signal to noise ratio on mammographic spectra: Monte Carlo simulation studies

“…Mammography, either for screening or for the examination of a woman who displays symptoms of breast cancer, must be capable of revealing subtle differences in density and composition of breast parenchymal tissue, as well as the presence of any abnormalities. This is a challenging imaging task since connective tissue, glandular tissue, skin and fat must be simultaneously visualized and differentiated, while having very similar attenuation coefficients and thus low subject contrast (C S ) [1]. Furthermore, it is also necessary to image objects of small size, such as microcalcifications, or of low contrast, such as masses.…”