Characterization of the breast region for computer assisted Tabar masking of paired mammographic images

Transcription

1 Characterization of the breast region for computer assisted Tabar masking of paired mammographic images Paola Casti, Arianna Mencattini, Marcello Salmeri Dept. of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy Maria Luisa Pepe, Fabio Mangieri, Antonietta Ancona Radiology Unit San Paolo Hospital of Bari, Italy { marialuisapep,mangeri.fabio }@libero.it, senologia.sanpaolo@asl.bari.it Abstract In this work we propose a joint two-side masking procedure for automatic analysis of mammographic images. The primary objective is the improvement of computerized systems capability in revealing additional findings, as the asymmetrical changes of the breast parenchyma. The method allows the proper comparison of the left and right breast by progressive selection of paired small areas on the mammogram, primarily the so-called ''forbidden areas", zones that need special attention in mammographic interpretation. The masking of specific areas of the mammogram requires the identification of two anatomical structures of the breast: the pectoral muscle and the nipple used, together with the breast skin line, to find paired matching points on the images for comparison. With this purpose, specific algorithms have been developed. In particular, a new method for nipple extraction will be presented and validated by expert radiologists, by the use of a proprietary program developed by the authors. Finally, an application example of the automatic Tabar masking procedure will be shown, in order to point out the potential of this method in detection of suspicious areas in mammograms. 1 Introduction The perception of subtle radiographic abnormalities in breast cancer screening can be improved by the use of a systematic approach to the analysis of mammograms, aimed at reducing false-positive rates and maintaining high levels of sensitivity. A complete mammographic study requires side-by-side viewing of corresponding areas of both breasts, whose practical realization should be strengthened by the technique of masking, as described by Tabar in [7], ensuring that all regions of the breast are viewed and compared in detail with the contralateral regions. An exhaustive Tabar masking would require at least four different analysis for each pair of views, performed with stepwise movements: horizontal and oblique masking of the Medio-Lateral Oblique (MLO) views, both in cranial and caudal direction; horizontal and vertical masking of the Cranio-Caudal (CC) views, the former in medial and lateral direction and the latter in proximal and distal direction. Particular attention should be given to the so-called "forbidden areas", specific areas where the majority of breast cancers is found in the early phase [7]: a) Milky area: the region parallel with the edge of the pectoral muscle on the MLO projection; b) Retroareolar area: the retroareolar region on the MLO projection; c) Medial-half area: the medial half of the breast on the CC projections; d) Retroglandular area: the retroglandular space on the CC projections. During the procedure, different regions of the mammogram are compared by a radiologist with step-by-step movements. At each step the areas under investigation can be matched singularly, changing stepwise the analyzed regions (Stepwise Tabar masking), or gradually, increasing the size of the paired observation windows (Incremental Tabar masking). The first approach enhances the perception of focal anomalies, while the latter allows a better understanding of global changes in the breast parenchyma. The objective of this work is the design of an automatic Tabar masking procedure for computer-aided detection systems, that could serve as an aid for radiologists in the identification of mammographic anomalies /12/$ IEEE

2 2 Computerized Tabar masking procedure Following radiologists' criteria in the interpretation of mammograms, the automatic Tabar masking procedure that we propose, includes four kind of masking, each one focused on one of the "forbidden areas" previously described. In particular, vertical and horizontal masking of the CC views and two oblique masking of the MLO views, as shown in Fig. I, could increase the sensitivity of computerized systems in detecting abnormalities on the breast region. The procedure, that is mainly oriented to the analysis of breast asymmetries, requires effective matching between the regions of the breast that will be compared at each step. Hence, paired matching points on the mammograms are required. To accomplish this goal the breast skin line, the pectoral muscle (only in Medio-Lateral Oblique (MLO) views), and the nipple need to be accurately identified for being the starting areas of the masking and serving as matching points for comparison (see Fig. I). As a consequence, a proper characterization of the breast region is the key point of the masking procedure and requires the development of dedicated algorithms that will be presented in the following sections. Once the breast region has been characterized, stepwise and incremental Tabar masking are both possible, ensuring the localization of all the subsequent analysis on the paired small areas extracted during the masking. The whole procedure is composed of the following steps: pectoral muscle extraction (in MLO views), breast skin line extraction, nipple detection, matching of the paired breast regions, Tabar masking (stepwise/incremental). 3 Dataset In this work, a total of 566 mammographic images have been used for validation. The dataset includes 369 Screen Film Mammography (SFM) images, 90 of which from the minimias database [6] and 279 from the DDSM database [2], together with 197 Full Field Digital Mammographic (FFDM) images from San Paolo Hospital of Bari. 4 Automatic characterization of the breast region 4.1 Pectoral muscle extraction (a) (b) Figure 1. Schematic representation of the proposed Tabar masking procedure for Cranio-Caudal views (a) and Medio-Iateral Oblique views (b). The preliminary step of the whole breast region characterization is the automatic extraction of the pectoral muscle in MLO views. An incorrect identification of the pectoral muscle can strongly compromise the detection of the nipple location, influencing the masking and all subsequent analysis results. The proposed method is based on the procedure described in [5]. It can be divided into three steps. Firstly, the image is decomposed using Gabor wavelets and the magnitude and phase images are computed by vector summation (first step). In the second step, points having opposite phase orientation are identified in a preliminary search for boundaries. Then, a binarization with adaptive threshold is performed obtaining possible candidates for the pectoral muscle contour. The third step of the algorithm implements the reduction of the false lines detected at the second step, by the application of five different logical conditions that are combined using the AND operator. The orientation, the position, the length of each line and the local mean intensity values along and around each line detected, are included for selecting the most probable muscles profile. The largest one is finally chosen for being the pectoral boundary. An example of the pectoral muscle extraction procedure is shown in Fig. 2. Least-squares regression slope and intercept have been computed from the pectoral boundary to obtain the starting line for one of the two oblique maskings in MLO views.

3 Original Image Gabor Magnitude False nipples reduction graphic (FFDM) images (see Fig. 3). Further details can be found in [5] and references therein. 4.3 Nipple detection Figure 2. (a) Original image. (b) Magnitude of Gabor wavelets after vector summation. (c) Candidates for pectoral muscle boundary. The red curve identifies the correct boundary while the white curves locates the removed false boundaries. 4.2 Breast skin line extraction The breast skin line extraction is needed to segment the breast region from the rest of the image (background) and it is a fundamental step for nipple detection. Unfortunately, the accurate identification of the breast boundary is even more complex in Screen Film Mammographic (SFM) images, where the boundary between the breast tissue and the film is blurred due to phenomena such as scattering and scanner digitization. The advent of digital mammography has made available a specific format of digital mammographic images, called for presentation, that is a result of a preliminary analysis, including noise reduction, directly performed by the acquisition system. For these images the boundary can be easily extracted by a simple image binarization with adaptive threshold. Hence, the procedure will be different in relation to the mammogras analyzed, Screen Film Mammography (SFM) or Full Field Digital Mammo- SFDMimages FFOM images,----- i:m7"as"7m-;:----o s:::a;:-- --',,' \ D!! Adaptive histogra m II thresholding using mean : : intensity II '---"''' ''- N '- hi -". -,,, - m --' I: thresholdingusingotsu.----, : l method r----':":::;----n-----l i ----n-----,, ; Figure 3. A flowchart of the whole breast boundary extraction procedure. I The pectoral muscle extraction and the breast skin line identification steps produce two binary masks whose intersection locate those points belonging to the breast tissue. The average orientation of the pectoral muscle will be also useful in the nipple detection. A novel method for the identification of the nipple location is presented, that follows the procedure described below: i) Rotate the image so that the pectoral muscle is at the bottom of the image and aligned to the horizontal axis. This results in an automatic rotation of ± 90 degrees in CC images and of an angle equal to the average orientation of the pectoral muscle in MLO images (see Fig.4(a». ii) Apply a top-hat transform to the rotated image with a circular-shaped structure element with a radius equal to 30 px (that at a spatial resolution of 300 J-Lm is equivalent to 9 mm ) to reduce the presence of non circular structures (see Fig. 4(b». iii) Extract an areolar segment around the area where you are likely to find the nipple. This segment is derived as follows: make a shift up and down the mask of the breast resulting in two masks which we call eroded and dilated masks, respectively; the intersection area of the two masks is a moon-shaped region containing the nipple. The region is further bounded below by a horizontal cut at the highest point of the eroded mask (see Fig. 4(c». iv) Apply a gaussian smoothing filter in order to reduce noise in the image with a sigma equal to 10 px (see Fig.4(d». v) Apply a nodal analysis computing the eigenvalues of the Hessian matrix derived from the image obtained at point iv) (see Fig. 4(e». Further details on the nodal analysis implementation can be found in [4]. vi) Perform a false nipples reduction, applying the following conditions on the possible candidates: retain only those candidates whose condition number (i.e., the ratio between the largest and the smallest two eigenvalues of each pixel in the image) is smaller than 2; then, retain only those candidates who exhibit the Eigenvalues Intensity Values (i.e., the product of the two eigenvalues of each pixel in the image) larger than the 60% of all the values; at the end, keep the only candidate in the original image where the luminance is greater (see Fig.4(f).

4 Original Image Top-hat transform Nipple areolar segment (a) (b) (c) Gaussian Smoothing filter Gradient vector fields False Nipple Reduction (d) Figure 4. Nipple detection procedure. (a) Original Image. (b) Top-hat transform with a circular-shaped structure element. (c) Areolar segment extraction. (d) Gaussian Smoothing filter. (e) Gradient vector field. (f) False nipples reduction. Once identified, nipple location will serve as a matching point for comparison in CC views Tabar masking and as the starting point for the remaining oblique Tabar masking of the MLO views. 5 Results and comparisons In order to evaluate the performance of the procedure for nipple detection, the approach described by Kinoshita in [3] has been used for comparison. It is based on the following assumptions: the nipple is located on the breast boundary, the glandular tissues contain structures (lobes, ducts, etc.) converging toward the nipple, the nipple appears as a bright (radio-opaque) region whereas adipose and low-density tissues appear as darker regions in the mammogram. It is the authors' opinion that these assumptions could lead to a false nipple detection when parenchymal distortion, breast skin line retraction, or opacities are present in a mammogram. Kinoshita et ai. used morphological top-hat filter to the mammogram to enhance the breast structures converging toward the nipple, using a disk-shaped structuring element with a radius of 50 px. This step is important because it enhances foreground while darkening background tissue. Then, the Radon transform is applied to the result of the top-hat filter with a limited angle interval equal to ±45 ± a :s: () :s: ±135 ± a in MLO views and ±45 :s: () :s: ± 135 in CC views, where a is the direction of the pectoral muscle edge using a straight-line approximation, () is the angle in the Radon domain, ± refers to right and left breasts respectively. The point in the mammogram containing the highest number of converging lines is expected to be close to the nipple. The position of the nipple is finally extracted searching the line with the maximum intensity that crosses the point of convergence and located to the point on the breast boundary reached by that straight line. In order to solve the problems occurring in the method by Kinoshita et ai., we firstly propose two modified versions. The first alternative approach is aimed to make the computation of the convergence more robust, considering not the point with the highest number of converging lines in all the image but the point falling in a sliding rectangular portion of the breast accounting for the presence of spurious convergence points falling in a region with an average high number of converging lines. The second alternative method, restricts the region where to extend the convergence line in a restricted ribbon domain in order to avoid wrong positioning of the nipple. The three methods are compared in Fig. 5, where improvements respect to the original version are reported. The results of the application of

5 KINOSHITA ET AL (a) (b) (c) Figure 5. Results of the three methods for nipple detection used for comparison. (a) Kinoshita et al. (b) Modified version 1 (b) Modified version 2 the four nipple detection methods to 566 images have been evaluated by computing the distance of the automatically detected nipple from the nipple manually located by expert radiologists using a proprietary program developed by the authors. The distances obtained have been grouped into five classes: < 5 mm, [5, 10] mm, [10, 15] mm, [15, 20] mm and> 20 mm and separately represented for the three different datasets and the four methods. Note that the proposed method produces better results for the FFDM images, which are those of greatest interest, while the two modified versions yield better performance with SFM images. The minimias dataset presents the major difficulties owing to the fact that in many images the nipple is not visible since it has been deleted by a preprocessing of the image. In that case, the radiologist has placed from5to 10mm _ from10to 15mm _from15to20m NODAL ANALYSIS KINOSHITA ET Al. MODIFIED 1 MODIFIED 2 (a) Figure 6. Histogram showing the distances in millimeters in the detection of the nipple, as compared to the same identified by a radiologist relative to the four considered methods the nipple at the point where the nipple would have been if it had not been eliminated by preprocessing. Conversely, many DDSM images present a high level noise due to digitalization process that may confound the nipple detection procedure. As a preliminary result of this study, Fig. 7 reports a possible application of the incremental Tabar masking procedure on two pairs of MLO and CC images from the FFDM dataset. In this case, each area of the mammogram is analyzed using a set of equally spaced real Gabor filters oriented over the half-closed angular interval ( -7r /2, 7r /2] (see [1] for more details). The scale of the filter relative to the chosen gaussian kernel is T = 11 and its elongation I = 3. The information content of the left and right corresponding masked regions has been used to compute the rose diagrams of the directional components shown in Fig. 7, plotting the magnitude response of the Gabor filter in the direction of the phase. Each pair of masked area is then compared extracting some statistical features on the difference of the angular distribution. In particular, the measure of the scatter of the distribution given by the Entropy, the average value of the angular distribution given by the First Angular Moment and the measure of the angular dispersion provided by the Second Angular Moment, are relevant features for the identification of abnormalities on the mammograms in Fig. 7(a). Fig. 7(b) shows the result of the directional analysis, performed using the features mentioned above. Higher values of the statistical features provides a clues about the presence of an asymmetry between the left and the right breast analyzed and lead to the detection of a suspicious area between the areas resulting from the Tabar masking. 6 Conclusions A novel method for nipple detection has been presented and remote validation of results has been made possible by the use of a proprietary program. The proposed automatic

6 '.:.:.,. :;,.:. -. :. -..., ". (a) MWlillUJ tej I Suspicious Areas Detected.. cc.. '... ""'m'm"';;" , '';'";'',,,,; ",', '..... ' '" ' m; '; ',t;, ' '".. f : j:ft,i!::r-===l 'illw ;: ::3 f luuj, " lnerementai step loaementai step'.... ' I cre ;"e-'; tai ste' p '. (b) Figure 7. Incremental Tabar masking applied on Gabor filtered images and relative rose diagrams of the angular distribution of the matching areas of the breast (a). Analysis of the difference angular distribution and identification of suspicious areas (b). Tabar masking procedure could lead to significant improvements in terms of algorithm's performances in the identification of bilateral asymmetries. Other kind of abnormalities including opacities, calcifications, or distortions can be identified by a two-steps procedure: restrict the candidate suspicious areas by masking and then, apply a specific-local analysis in the suspicious areas to detect possible abnormalities and locate them. Incremental Tabar masking exhibits a higher robustness to image deformation due to compression or misalignment than stepwise masking procedure. References [I] P. Casti. Development and validation of a computer-aided detecion system for the identification of bilateral asymmetry in mammographic images. Master's thesis, University of Rome Tor Vergata, 20 II. [2] M. Heath, K. Bowyer, D. Kopans, et al. Current status of the digital database for screening mammography. Digital Mammography, pages , [3] S. Kinoshita, P. Azevedo-Marques, R. R. Pereira, J. Rodrigues, and R.M.Rangayyan. Radon-domain detection of the nipple and the pectoral muscle in mammograms. Journal of Digital Imaging, 21(1):37-49,2008, [4] A. Mencattini and M. Salmeri. Breast masses detection using phase portrait analysis and fuzzy inference systems. International Journal of Computer Assisted Radiology and Surgery, in press. [5] A. Mencattini, M. Salmeri, P. Casti, M. L. Pepe, F. Mangieri, and A. Ancona. Local active contour models and gabor wavelets for an optimal breast region segmentation. In Computer Assisted Radiology and Surgey (CARS' 12), Pisa, Italy, June [6] J. Suckling et al. The mammographic image analysis society digital mammogram database exerpta medica. International Congress Series 1069, pages , [7] L. Tabar, T. Tot, and P. Dean. Breast cancer, the art and science of early detection with mammography: perception, interpretation, histopathologic correlation. Verlag, George Thieme