Juan J. Vidal

Automatic detection of breast border and nipple in digital mammograms

Advances in the area of computerized image analysis applied to mammography may have very important practical applications in automatically detecting asymmetries (masses, architectural distortions, etc.) between the two breasts. We have developed a fully automatic technique to detect the breast border and the nipple, this being a necessary prerequisite for further image analysis. To detect the breast border, an algorithm that computes the gradient of gray levels was applied. To detect the nipple, three algorithms were compared (maximum height of the breast border, maximum gradient, and maximum second derivative of the gray levels across the median-top section of the breast). A combined method was also designed. The algorithms were tested on 156 digitized mammograms. The breast segmentation results were evaluated by two expert radiologists and one physicist. In 89% of the mammograms, the computed border was in close agreement with the radiologists estimated border. Segmentation results were acceptable to be used in computer-aided diagnostic schemes. The mean distance between the position of the nipple indicated by two radiologists by consensus and the position calculated by the computer was 6 mm.

Computer‐aided diagnosis: Automatic detection of malignant masses in digitized mammograms

A computerized method to automatically detect malignant masses on digital mammograms based on bilateral subtraction to identify asymmetries between left and right breast images was developed. After the digitization, in order to align left and right mammograms the breast border and nipple were automatically detected. Images were corrected to avoid differences in brightness due to the recording procedure. Left and right mammograms were subtracted and a threshold was applied to obtain a binary image with the information of suspicious areas. The suspicious regions or asymmetries were delimited by a region growing algorithm. Size and eccentricity tests were used to eliminate false-positive responses and texture features were extracted from suspicious regions to reject normal tissue regions. The scheme, tested in 70 pairs of digital mammograms, achieved a true-positive rate of 71% with an average number of 0.67 false positives per image. Computerized detection was evaluated by using free-response operating characteristic analysis (FROC). An area under the AFROC (A1) of 0.667 was obtained. Our results show that the scheme may be helpful to the radiologists by serving as a second reader in mammographic screening. The low number of false positives indicates that our scheme would not confuse the radiologist by suggesting normal regions as suspicious.

Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.

Enhancement of chest and breast radiographs by automatic spatial filtering

The authors present a new algorithm to enhance the edges and contrast of chest and breast radiographs while minimally amplifying image noise. The algorithm consists of a linear combination of an original image and two smoothed images obtained from it by using different masks and parameters, followed by the application of nonlinear contrast stretching. The result is an image which retains the high median frequency local variations (edge and contrast-enhancing).

Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns.

We have developed a method for the quantification of breast texture by using different algorithms to classify mammograms into the four patterns described by Wolfe (N1, P1, P2 and Dy). The computerized scheme employs craniocaudal views of conventional screen-film mammograms, which are digitized by a laser scanner. We used discriminant analysis to select among different feature-extraction techniques, including Fourier transform, local-contrast analysis, and grey-level distribution and quantification. The method has been evaluated on 117 clinical mammograms previously classified by five radiologists as to mammographic breast parenchymal patterns (MBPPS). The results show differences in agreement among radiologists and computer classification, depending on the Wolfe pattern: excellent for Dy (kappa = 0.77), good for P2 (kappa = 0.52) and N1 (kappa = 0.52) and poor for P1 (kappa = 0.22). Our quantitative texture measure as calculated from digital mammograms may be valuable to radiologists in their assessment of MBPP and therefore useful in establishing an index of risk for developing breast carcinoma.

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images

We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.

Automatic calculation of total lung capacity from automatically traced lung boundaries in postero‐anterior and lateral digital chest radiographs

Total lung capacity (TLC) is a very important parameter in the study of pulmonary function. In the pulmonary function laboratory, it is normally obtained using plethysmography or helium dilution techniques. Several authors have developed methods of calculating the TLC using postero-anterior (PA) and lateral chest radiographs. These methods have not been often used in clinical practice. In the present work, we have developed and automated computer-based method for the calculation of TLC, by determining the pulmonary contours from digital PA and lateral radiographs of the thorax. The automatic tracing of the pulmonary borders is carried out using: (1) a group of reference lines is determined in each radiograph; (2) a family of rectangular regions of interest (ROIs) defined, which include the pulmonary borders, and in each of them the pulmonary border is identified using edge enhancement and thresholding techniques; (3) removing outlaying points from the preliminary boundary set; and (4) the pulmonary border is corrected and completed by means of interpolation, extrapolation, and arc fitting. The TLC is calculated using a computerized form of the radiographic ellipses method of Barnhard. The pulmonary borders were automatically traced in a total of 65 normal radiographs (65 PA and 65 lateral views of the same patients). Three radiologists carried out a subjective evaluation of the automatic tracing of the pulmonary borders, with a finding of no error or only one minor error in 67.7% of the PA evaluations, and in 75.9% of the laterals. Comparing the automatically traced borders with borders traced manually by an expert radiologists, we obtained a precision of 0.990 +/- 0.001 for the PA view, and 0.985 +/- 0.002 for the lateral. The values of TLC obtained by the automatic calculation described here showed a high correlation (r = 0.98) with those obtained by applying the manual Barnhard method.

Evaluation of an automated wavelet-based system dedicated to the detection of clustered microcalcifications in digital mammograms

Mammographic screening programs are delivering reductions in breast cancer mortality. However, breast cancer screening will be cost effective and will provide a real profit only when both high sensitivity and specificity levels are reached. To date, due to human or technical factors, a significant number of breast cancers are still missed or misinterpreted on the mammograms. Computer methodologies, developed to assist radiologists, could represent further amelioration by increasing diagnostic accuracy in the screening programs. We have tested a computerized scheme to detect clustered microcalcifications in digital mammograms, employing 360 mammograms that were randomly selected from the mammographic screening program, currently undergoing at the Galicia Community (Spain). After the digitization process, the breast border was initially determined. A wavelet-based algorithm was employed to detect the clusters of microcalcifications. The performance of the automated system over the test set was evaluated employing Free-response Receiver Operating Characteristic (FROC) methodology. The sensitivity achieved was 74% at a false positive detection rate of 1.83. The corresponding area under the Alternative FROC (AFROC) curve was A1=0.667 +/-0.09.Mammographic screening programs are delivering reductions in breast cancer mortality. However, breast cancer screening will be cost effective and will provide a real profit only when both high sensitivity and specificity levels are reached. To date, due to human or technical factors, a significant number of breast cancers are still missed or misinterpreted on the mammograms. Computer methodologies, developed to assist radiologists, could represent further amelioration by increasing diagnostic accuracy in the screening programs. We have tested a computerized scheme to detect clustered microcalcifications in digital mammograms, employing 360 mammograms that were randomly selected from the mammographic screening program, currently undergoing at the Galicia Community (Spain). After the digitization process, the breast border was initially determined. A wavelet-based algorithm was employed to detect the clusters of microcalcifications. The performance of the automated system over the test set was evaluated employing Free-response Receiver Operating Characteristic (FROC) methodology. The sensitivity achieved was 74% at a false positive detection rate of 1.83. The corresponding area under the Alternative FROC (AFROC) curve was A 1 =0.667 - 0.09.

Real and simulated clustered microcalcifications in digital mammograms. ROC study of observer performance.

We have developed a model to simulate clustered microcalcifications on digital mammograms. Wavelet transform techniques were used to detect real clustered microcalcifications. A feature analysis process was applied to automatically extract the features describing the individual simulated microcalcifications and clusters from the values of the real clustered microcalcifications present in the mammogram. Subsequently, a database of simulated and real clustered microcalcifications was created. Clusters of microcalcifications from this database were tested for indistinguishability from real ones. Two radiologists and one physicist were asked to indicate whether the microcalcifications were either real or simulated. The responses of the readers were evaluated with a ROC analysis and the area under the curve was calculated. The average ROC area was 0.54 +/- 0.03, indicating there was no statistical difference between real and simulated clustered microcalcifications. The method allows for the creations of simulated clustered microcalcifications that are virtually indistinguishable from real microcalcifications in digital mammograms and could be used to evaluate different image processing techniques.

Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms

The functionalities of the JPEG2000 standard have led to its incorporation into digital imaging and communications in medicine (DICOM), which makes this compression method available for medical systems. In this study, we evaluated the compression of mammographic images with JPEG2000 (16:1, 20:1, 40:1, 60.4:1, 80:1, and 106:1) for applications with a computer-aided detection (CAD) system for clusters of microcalcifications. Jackknife free-response receiver operating characteristic (JAFROC) analysis indicated that differences in the detection of clusters of microcalcifications were not statistically significant for uncompressed versus 16:1 (T=-0.7780;p=0.4370),20:1(T=1.0361;p=0.3007), and 40:1 (T=1.6966;p=0.0904); and statistically significant for uncompressed versus 60.4:1 (T=5.8883;p<0.008), 80:1 (T=7.8414;p<0.008), and 106:1 (T=17.5034;p=<0.008). Although there is a small difference in peak signal-to-noise ratio (PSNR) between compression ratios, the true-positive (TP) and false-positive (FP) rates, and the free-response receiver operating characteristic (FROC), figure of merit values considerably decreased from a 60:1 compression ratio. The performance of the CAD system is significantly reduced when using images compressed at ratios greater than 40:1 with JPEG2000 compared to uncompressed images. Mammographic images compressed up to 20:1 provide a percentage of correct detections by our CAD system similar to uncompressed images, regardless of the characteristics of the cluster. Further investigation is required to determine how JPEG2000 affects the detectability of clusters of microcalcifications as a function of their characteristics