Optimization of the display of digital mammograms is an important challenge and requires knowledge of the characteristics of actual patient images. This work aims to create a description of some of the fundamental statistical properties of a large volume of images acquired on an FDA approved device as used in clinical practice. 4569 digital mammograms (1246 patients) were acquired between October
2001 and August 2002 on a GE Senograph 2000D at Sunnybrook and Women's College Health Sciences Centre. Images were saved in "raw" format. The breast was then segmented from the background on the image using a technique based on thresholding and some connectivity rules. The histogram of pixel values in the breast only is then
calculated for both the raw and processed versions of the image. The region of constant thickness, where the breast is in contact with the compression paddle, was also segmented from the CC view raw images. The histogram and statistical properties in this central region were also calculated. Assorted statistical descriptors of the histograms were examined (dynamic range, mean, standard deviations, median and mode). The effect of image processing on the dynamic range in the periphery and central area of the breast was evaluated.
The results were compared against the automatic exposure algorithm and acquisition parameters, projection (view) and breast thickness.
For digital mammography to be efficient, methods are needed to choose an initial default image presentation that maximizes the amount of relevant information perceived by the radiologist and minimizes the amount of time spent adjusting the image display parameters. The purpose of this work is to explore the possibility of using the output of computer aided detection (CAD) software to guide image enhancement and presentation. A set of 16 digital mammograms with lesions of known pathology was used to develop and evaluate an enhancement and display protocol to improve the initial softcopy presentation of digital mammograms. Lesions were identified by CAD and the DICOM structured report produced by the CAD program was
used to determine what enhancement algorithm should be applied in the identified regions of the image. An improved version of contrast limited adaptive histogram equalization (CLAHE) is used to enhance calcifications. For masses, the image is first smoothed using a non-linear diffusion technique; subsequently, local contrast is enhanced with a method based on morphological operators. A non-linear lookup table is automatically created to optimize the contrast in the regions of interest (detected lesions) without losing the context of the periphery of the breast. The effectiveness of the enhancement
will be compared with the default presentation of the images
using a forced choice preference study.
This paper proposes a novel algorithm for mammographic image enhancenment, based on identifying the peripheral region of the breast and suppressing the large change in signal caused by reduction of thickness there, while maintaining the local contrast information related to tissue composition. The thickness compensation algorithm consists of three processing steps. The first step is to generate a thickness map using two phantoms, one which simulates the shape of the breast in the cranio-caudal projection and a second one as a triangular attenuator. The second step is to warp the phantom thickness map in the peripheral region to that of the breast image. The third step is to equalize the signal values in the peripheral region relative to the signal in the uniform thickness area using the warped thickness map data. Examples are presented to show the effectiveness of the proposed method in effectively suppressing the large range of signal caused by thickness changes in the peripheral region, thereby facilitating image presentation and analysis. The performance of the proposed algorithm was also evaluated on clinical mammograms by computing volumetric breast density.