A digital density equalization technique to improve visualization of breast periphery in mammography

The nipple-areolar complex: comprehensive imaging review

The nipple-areolar complex can be affected by a variety of benign and malignant entities that can present with non-specific symptoms. Benign pathologies commonly affecting the nipple-areolar complex include nipple calcifications, nipple adenoma, abscess of Montgomery tubercles, ductal ectasia, periductal mastitis, and papilloma. Malignant pathologies that affect the nipple-areolar complex include Paget's disease of the breast, ductal carcinoma in-situ, and invasive ductal carcinoma. Clinical history and examination, imaging, and tissue sampling when appropriate are co-dependent factors that guide the assessment of nipple-areolar pathologies. This article provides a review of the normal anatomy, common anatomical variants, benign and malignant pathologies, and imaging techniques to guide the diagnostic assessment of the nipple-areolar complex.

Automatic detection of abnormalities in mammograms

Background

In recent years, an increased interest has been seen in the area of medical image processing and, as a consequence, Computer Aided Diagnostic (CAD) systems. The basic purpose of CAD systems is to assist doctors in the process of diagnosis. CAD systems, however, are quite expensive, especially, in most of the developing countries. Our focus is on developing a low-cost CAD system. Today, most of the CAD systems regarding mammogram classification target automatic detection of calcification and abnormal mass. Calcification normally indicates an early symptom of breast cancer if it appears as a small size bright spot in a mammogram image.

Methods

Based on the observation that calcification appears as small bright spots on a mammogram image, we propose a new scale-specific blob detection technique in which the scale is selected through supervised learning. By computing energy for each pixel at two different scales, a new feature “Ratio Energy” is introduced for efficient blob detection. Due to the imposed simplicity of the feature and post processing, the running time of our algorithm is linear with respect to image size.

Results

Two major types of calcification, microcalcification and macrocalcification have been identified and highlighted by drawing a circular boundary outside the area that contains calcification. Results are quite visible and satisfactory, and the radiologists can easily view results through the final detected boundary.

Conclusions

CAD systems are designed to help radiologists in verifying their diagnostics. A new way of identifying calcification is proposed based on the property that microcalcification is small in size and appears in clusters. Results are quite visible and encouraging, and can assist radiologists in early detection of breast cancer.

[How to explore breast skin lesion?: Guidelines]

A change in the shape or appearance of the nipple-areola complex, especially if it is unilateral and recent appearance, is a sign of underlying breast tumor. Breast imaging is then required (grade C). Any erythematous lesion of the nipple or nipple-areola can be a Paget's disease, an adenoma of the nipple or a nipple eczema. Clinical course and pattern can point to a diagnosis without sufficient specificity (LE4). If nipple eczema is suspected, it is recommended to perform a test treatment with topical corticosteroids. In case of failure or if a Paget's disease of the breast is suspected, a biopsy must be done. When indicated, it is not possible to recommend a biopsy modality (scrape cytology, punch biopsy, nipple core biopsy and surgical excision) compared to another. When imaging exploration of the nipple-areola complex is necessary, ultrasound and mammography are recommended as first-line. In the absence of signal, an MRI is recommended as second-line (grade C).

Breast peripheral area correction in digital mammograms

Digital mammograms may present an overexposed area in the peripheral part of the breast, which is visually shown as a darker area with lower contrast. This has a direct impact on image quality and affects image visualisation and assessment. This paper presents an automatic method to enhance the overexposed peripheral breast area providing a more homogeneous and improved view of the whole mammogram. The method automatically restores the overexposed area by equalising the image using information from the intensity of non-overexposed neighbour pixels. The correction is based on a multiplicative model and on the computation of the distance map from the breast boundary. A total of 334 digital mammograms were used for evaluation. Mammograms before and after enhancement were evaluated by an expert using visual comparison. In 90.42% of the cases, the enhancement obtained improved visualisation compared to the original image in terms of contrast and detail. Moreover, results show that lesions found in the peripheral area after enhancement presented a more homogeneous intensity distribution. Hence, peripheral enhancement is shown to improve visualisation and will play a role in further development of CAD systems in mammography.

Evaluation of detector dynamic range in the x-ray exposure domain in mammography: a comparison between film-screen and flat panel detector systems

Digital detectors in mammography have wide dynamic range in addition to the benefit of decoupled acquisition and display. How wide the dynamic range is and how it compares to film-screen systems in the clinical x-ray exposure domain are unclear. In this work, we compare the effective dynamic ranges of film-screen and flat panel mammography systems, along with the dynamic ranges of their component image receptors in the clinical x-ray exposure domain. An ACR mammography phantom was imaged using variable mAs (exposure) values for both systems. The dynamic range of the contrast-limited film-screen system was defined as that ratio of mAs (exposure) values for a 26 kVp Mo/Mo (HVL=0.34 mm Al) beam that yielded passing phantom scores. The same approach was done for the noise-limited digital system. Data from three independent observers delineated a useful phantom background optical density range of 1.27 to 2.63, which corresponded to a dynamic range of 2.3 +/- 0.53. The digital system had a dynamic range of 9.9 +/- 1.8, which was wider than the film-screen system (p<0.02). The dynamic range of the film-screen system was limited by the dynamic range of the film. The digital detector, on the other hand, had an estimated dynamic range of 42, which was wider than the dynamic range of the digital system in its entirety by a factor of 4. The generator/tube combination was the limiting factor in determining the digital system's dynamic range.

A digital equalisation technique improving visualisation of dense mammary gland and breast periphery in mammography

INTRODUCTION

In mammographic imaging, use of high contrast screen-film combinations results in under-exposed and over-exposed film areas corresponding to dense mammary gland and breast periphery (BP), respectively, characterised by degraded image contrast. A digital equalisation technique was designed and developed in order to deal with the problem of poor visualisation of these regions.

METHODS AND MATERIAL

The technique is based on the film-digitiser characteristic curve and a layer model of the breast region, as depicted on a mammogram. It remaps each layer grey level (GL) values by a correction factor that accounts for thickness variation in BP and the presence of dense fibroglandular tissues at the mammary gland. The major steps of the technique are segmentation, to isolate the breast region from mammogram background, and adaptive layer GL remapping.

RESULTS

The performance of the technique was initially evaluated on a sample of 60 mammograms. Comparative evaluation between the initial and processed images was performed on the basis of nine anatomical features situated at dense mammary gland and BP. The mammographic images resulting from application of the proposed technique are GL equalised and the visualisation improvement of all anatomical features was found to be statistically significant (P<0.05) or highly significant (P<0.0001). The proposed technique was also compared with contrast-limited adaptive histogram equalisation (CLAHE) and found to be more effective in the visualisation of all anatomical features examined, for both dense breast (DB) and BP.

DISCUSSION AND CONCLUSION

Application of the proposed technique results in improved visualisation of both dense mammary gland and BP regions. The proposed technique is independent of breast size, breast symmetry and mammographic view. The technique contributes to breast dose minimisation by eliminating the need for a second acquisition.

Model-based technique for the measurement of skin thickness in mammography

A model-based method is proposed for the measurement of breast skin thickness from digitised mammograms that takes into account both the geometric and radiographic properties of the skin region. The method initially identifies a salient feature that discriminates the skin from the other anatomical structures of the breast. Its identification is based on a multi-scale grey-level gradient estimation, using a wavelet decomposition of the image. The spatial distribution of this feature is organised as a graph, with each of its nodes associated with a binary set of interpretation labels. A Markov random field is defined on the set of labels, and the best graph labelling is finally determined with a maximum a posteriori (MAP) probability criterion. The method was applied on 11 mammograms with improved contrast characteristics at the breast periphery, obtained by an exposure equalisation technique during image acquisition. The validation of the approach was performed by calculating the root mean square (RMS) error between the detected skin thickness and manual measurements performed on each of the films. The resulting error values ranged from 0.1 mm to 0.2 mm for normal cases and reached a maximum of 0.5mm in pathological cases with advanced skin thickening.

Quantifying image quality at breast periphery vs mammary gland in mammography using wavelet analysis

Use of high contrast film-screen systems in mammography, in combination with the fact that exposure parameters are selected to ensure good visualization of the mammary gland, results in overexposure of the film area corresponding to the breast periphery, therefore decreasing image quality. The aim of this work was to provide a quantitative evaluation of image quality at the breast periphery compared with the mammary gland. To deal with the difficulties in quantification of image quality introduced by low contrast encountered at the breast periphery, wavelet analysis has been used for derivation of a contrast indicator (CI) and a noise indicator (NI), taking into account local grey level variations. Gradient magnitude coefficients corresponding to region of interest (ROI) grey level values are the basis of CI definition. Mammary gland and breast periphery were sampled by equally spaced ROIs, the quantity of which was determined by a heuristic method. For NI definition, the power values of gradient magnitude coefficients corresponding to the ROI were utilized. Image quality at the breast periphery compared with the mammary gland was evaluated using 150 craniocaudal images from the Digital Database for Screening Mammography. Measurements were carried out using a tool developed in our department. A 50% contrast decrease at the breast periphery was observed, while noise decreased by approximately 2%.