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P G Tahoces

Publications and source records attributed to P G Tahoces.

12 recordsLinked to original sources
PubMed1999

A wavelet-based algorithm for detecting clustered microcalcifications in digital mammograms.

A computerized scheme to detect clustered microcalcifications in digital mammograms has been developed. Detection of individual microcalcifications in regions of interest (ROIs) was also performed. The mammograms were previously classified into fatty and dense, according to their breast tissue. The most appropriate wavelet basis and reconstruction levels were selected. To select the wavelet basis, 40 profiles of microcalcifications were decomposed and reconstructed using different types of wavelet functions and different combinations of wavelet coefficients. The symlets with a basis of length 8 were chosen for fatty tissue. For dense tissue, the Daubechies' wavelets with a four-element basis were employed. Two methods to detect individual microcalcifications were evaluated: (a) two-dimensional wavelet transform, and (b) one-dimensional wavelet transform. The second technique yielded the best results, and was used to detect clustered microcalcifications in the complete mammogram. When detecting individual microcalcifications by using two-dimensional wavelet transform we have obtained, for fatty ROIs, a sensitivity of 71.11% at a false positive rate of 7.13 per image. For dense ROIs the sensitivity was 60.76% and the false positive rate, 7.33. The areas (A1) under the AFROC curves were 0.33+/-0.04 and 0.28+/-0.02, respectively. The one-dimensional wavelet transform method yielded 80.44% of sensitivity and 6.43 false positives per image (A1=0.39+/-0.03) for fatty ROIs, and 62.17% and 5.82 false positives per image (A1=0.37+/-0.02) for dense ROIs. For the detection of clusters of microcalcifications in the entire mammogram, the sensitivity was 80.00% with 0.94 false positives per image (A1=0.77+/-0.09) for fatty mammograms, and 72.85% of sensitivity at a false positive detection rate of 2.21 per image (A1=0.64+/-0.07) for dense mammograms. Globally, a sensitivity of 76.43% at a false positive detection rate of 1.57 per image was obtained.

Algorithms↗
PubMed1998

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.

Algorithms↗
PubMed1998

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.

Adolescent↗
PubMed1997

Computerized automatic system to obtain inspiration/expiration chest roentgenograms without patient collaboration.

We have developed a computerized automatic X-ray image acquisition system. It consists of conventional pressure and flow transducers connected to a commercial data acquisition card installed in a PC/AT, the card output being connected through a relay to a conventional X-ray system. Signal processing, that has been applied for automatic detection of inspiration and expiration in respiratory signals, allows the X-ray equipment activation at a given moment of the respiratory cycle. Radiographs are thus obtained either in inspiration or expiration at tidal volume on anaestesized experimental animals. The system has also been tested on non-collaborating patients.

Animals↗
PubMed1997

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.

Biophysical Phenomena↗
PubMed1996

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 radiologist's 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.

Algorithms↗
PubMed1995

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.

Algorithms↗
PubMed1995

Digital chest radiography: comparison of unprocessed and processed images in the detection of solitary pulmonary nodules.

PURPOSE: To compare the accuracy with which simulated solitary pulmonary nodules can be identified on digital images of the chest that are unprocessed, processed with adaptive spatial filtering, or processed with global filtering. MATERIALS AND METHODS: Six experienced chest radiologists evaluated 408 test radiographs (136 from each of the three types, half with simulated nodules) and judged whether a nodule was present. Data from the 2,448 observations were evaluated by means of a receiver operating characteristic curve with location methods. RESULTS: Accuracy was significantly better with the adaptive filter technique than with the global technique (P < .05), and it was better with adaptive filtering than with no processing in the detection of pulmonary nodules in the mediastinal-subdiaphragmatic areas (P < .05). No significant difference was found between no processing and global filtering. CONCLUSION: Adaptive filtration is superior to global filtration in the identification of solitary pulmonary nodules and is superior to no processing in nodules projected over the radiopaque areas of the thorax on digital images.

Algorithms↗
PubMed1992

Enhancement of chest images by automatic adaptive spatial filtering.

Postprocessing of the image data is an exciting capability of digital radiography that may improve diagnostic performance. We present a new algorithm that selectively enhances edges and contrast in both lungs and mediastinum while minimally amplifying noise in chest images. Using different size kernels, two smoothed images are generated from the original chest image. The two regions of interest (lungs and mediastinum) are identified based on the distribution of pixel values in the image. A modified nonlinear unsharp mask subtraction technique is then applied. The resulting image has enhanced high- and middle-frequency information in the mediastinum without distorting lung parenchyma or significantly enhancing noise. We consider that the technique employed in this study could be suitable for routine use although its true effectiveness in improving diagnostic accuracy awaits observer-performance evaluation that is currently under way.

Adult↗
PubMedDate not supplied

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.

Algorithms↗
PubMedDate not supplied

A limited PACS dedicated to a research environment. Nine years' experience in Santiago de Compostela.

A digital image network has been installed at the Department of Radiology of the University of Santiago de Compostela, Spain, to create a "limited' Picture Archiving and Communication System (PACS). This experience is being dedicated to address problems associated with digital techniques in a research environment. The backbone of the system is a multiprotocol ethernet network. Attached to the network are a number of advanced devices such as DEC VAX and UNIX workstations. Currently, a high resolution film digitizer and a laser printer are under evaluation for radiologic image research. During a period of nine years, 1987 to 1995, experimental and clinical trials have been conducted on different film based digital radiography apparatus primarily dedicated to chest and breast imaging. Several research projects have been completed. In this paper we describe the results of these investigations and discuss the advantages and disadvantages of this digital technique. The results of the different completed studies will be presented separately. A description of the physical characteristics of the limited PACS system dedicated to a research environment will serve as background.

Computer Communication Networks↗