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

Breast image pre-processing for mammographic tissue segmentation

During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

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.

A quantitative method for evaluating the detectability of lesions in digital mammography

This study presents a quantitative method for evaluating the detectability of microcalcifications in digital mammography. Four hundred and twenty microcalcifications (with various morphology, size and contrast), simulated with a previously validated method, were used for the creation of image datasets. Lesions were inserted into 163 regions of interests of 59 selected raw digital mammograms with various anatomical backgrounds and acquired with a Siemens Novation DR. After processing, these composite images were scored by experienced radiologists, who located multiple simulated lesions and rated them under conditions of free-search. For statistical analysis, free-response receiver-operating characteristic curves are plotted; the use of jackknife free-response receiver-operating characteristic method has also been investigated. The main advantage of this methodology is that the exact number of inserted microcalcifications is well known and that the lesions are fully characterised in terms of pathology, size, morphology and peak contrast. A first application has been the evaluation of the effect of anatomical background on microcalcifications detection. Preliminary findings in this study indicate that this method may be a promising tool to evaluate factors that have an influence on the detectability of lesions, such as the clinical processing or the viewing conditions.

Differential use of image enhancement techniques by experienced and inexperienced observers

Full-field digital mammography (FFDM) systems are currently being used to acquire mammograms in digital format, but digital displays are less than ideal compared to traditional film-screen display. Certain physical properties of softcopy displays [e.g., modulation transfer function (MTF)] are less than optimal compared to film. We developed methods to compensate for some of these softcopy display deficiencies, based on careful physical characterization of the displays and image-processing software. A series of 100 FFDM and 60 digitized images was shown to six observers-half experienced (mammographers) and half inexperienced (radiology residents). The observers had to decide if a mass or microcalcification cluster was present and classify it as benign or malignant. A window could be activated that brought the image detail within the window to full resolution and corrected for the nonisotropic MTF of the Cathode Ray Tube (CRT) display. Experienced readers had better diagnostic performance and took less time to view the images. Experienced readers used window/level more than inexperienced readers, but inexperienced readers used magnification and the MTF compensation tool more often. Use of the magnification and the MTF tool increased reader decision confidence. Experienced and inexperienced readers use image-processing tools differently, with certain tools increasing reader confidence. Understanding how observers use image-processing tools may help in the development of better and more automated user interfaces.

Thickness correction of mammographic images by means of a global parameter model of the compressed breast

Peripheral enhancement and tilt correction of unprocessed digital mammograms was achieved with a new reversible algorithm. This method has two major advantages for image visualization. First, the display dynamic range can be relatively small, and second, adjustment of the overall luminance to inspect details is not required in most cases. The correction is useful for preprocessing in computer-aided detection/diagnosis algorithms. The method is based on knowledge of the three-dimensional compressed breast shape to equalize thickness by adding virtual tissue, which results in intensity equalization for the mammographic image. Previously described methods implicitly estimate the contribution of thickness variations to image intensity, usually by nonparametric methods. The proposed method employs a global parametic breast shape model, which is advantageous for visualization and CAD.