Biomedical subjects

A J Méndez

Publications and source records attributed to A J Méndez.

6 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↗

PubMed1999

[Gender differences and diabetic status in the relationship of blood apolipoprotein C-III, free fatty acids and triglycerides in subjects at risk for glucose intolerance].

Insulin resistance and hyperinsulinemia can induce overproduction of triglyceride (TG) rich VLDL in the liver by increasing the availability of free fatty acids (FFA). Conversely, apolipoprotein C-III (apoC-III) is an inhibitor of the catabolism of TG-rich lipoproteins. To explore the relationship among FFA, apo C-III and TG in hyperinsulinemic subjects, we studied 103 individuals (63 women and 40 men) with a body mass index (BMI) 25 Kg/m2: 59 subjects with normal glucose tolerance (NGT), and 44 with newly diagnosed type 2 diabetes. After adjustment for age, BMI, fasting insulin and TG, FFA were significantly higher in women than in men and in subjects with diabetes compared with NGT. Subjects with diabetes had higher apo C-III levels compared to NGT, adjusted for age, sex and BMI, and that was largely accounted for by differences in insulin and TG levels. In addition, regression analysis in subjects with diabetes showed that TG were strongly associated with apo C-III in both men and women (r = 0.90 and 0.79, respectively; p < 0.001), while the association tended to be smaller between TG and FFA (r = 0.48, p < 0.05 in men and r = 0.45, p = 0.06 in women). Conversely, in individuals with NGT fasting TG was strongly associated with apo C-III in men (r = 0.83, p < 0.01) but not with FFA, while in women TG was associated with FFA (r = 0.39, p < 0.05) but not with apo C-III. In summary, elevated apo C-III was a predominant factor associated with elevated TG levels in NGT men and all subjects with type 2 diabetes, while FFA were more closely related with TG levels in NGT women.

Adult↗

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↗

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↗

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↗