Mammogram acquisition in digital format is one of the most relevant steps for image processing in computer-aided detection schemes for mammography. We investigate film digitizer systems using different technologies to determine their influence on the results of mammography image segmentation schemes. It also provides image scanning process regardless of the technology by the development of automatic software based on the digitizers’ characteristic curves. Comparative assessment of digitizer properties and features is performed as well as the software for managing the digitized image acquisition. The images were obtained from six different digitizers and evaluated by means of statistical analysis. Tests were conducted for comparing the responses from each equipment, regarding their respective curves, and they have presented significant variations relative to the original characteristic curve of high quality films used as reference—which largely influence the performance of processing schemes applied on sets of mammography images digitized by those systems. However, when our proposed scanning software was applied with intensity transformation procedure based on the characteristic curve “correction,” the images were comparable to the film optical density, which has improved the processing technique’s performance. The results have pointed out it is possible to achieve high sensitivity and performance of such schemes even with low-cost digitizer systems since their quality characteristics are well known and the procedure herein proposed is used within the mammogram scanning process.
Taking into account requirements for processing digital mammograms, systems dealing with the optimization of images
acquisition need to be adequately evaluated. The processes for generating these images are varied and they can be
grouped mainly in two categories: (1) films scanned by specialized digitizers; (2) images obtained from electronic
sensors associated to digital converters (CR and DR systems). The main two types of different scanners are those with
white light-based detection and CCD sensors and with a scanning laser beam. Thus the current investigation aims to
perform quality evaluation of film digitizers, mainly addressed to mammography. In this analysis the following
parameters were studied: digitizers characteristic curves - relating the pixel value assigned to a region and the
corresponding optical density of the film on the same region; noise - obtained by the Wiener spectrum; and
reproducibility - evaluating whether a device used to capture a digital image can be reliable in subsequent scans. Six
different digitizer equipments were investigated with purposes of determining tools to enhance the image quality based
on their characteristics. The results have indicated that although the most sophisticated scanners have the best
characteristics among those evaluated, knowledge about the scanner behavior can allow developing procedures to
provide the adequate quality image for processing schemes.
We evaluated the performance of a novel procedure for segmenting mammograms and detecting clustered microcalcifications in two types of image sets obtained from digitization of mammograms using either a laser scanner, or a conventional “optical” scanner. Specific regions forming the digital mammograms were identified and selected, in which clustered microcalcifications appeared or not. A remarkable increase in image intensity was noticed in the images from the optical scanner compared with the original mammograms. A procedure based on a polynomial correction was developed to compensate the changes in the characteristic curves from the scanners, relative to the curves from the films. The processing scheme was applied to both sets, before and after the polynomial correction. The results indicated clearly the influence of the mammogram digitization on the performance of processing schemes intended to detect microcalcifications. The image processing techniques applied to mammograms digitized by both scanners, without the polynomial intensity correction, resulted in a better sensibility in detecting microcalcifications in the images from the laser scanner. However, when the polynomial correction was applied to the images from the optical scanner, no differences in performance were observed for both types of images.