The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROIs containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The authors' results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.

We studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred and sixty-eight regions of interest (ROIS) containing biopsy-proven masses and 504 ROIS containing normal breast tissue were extracted from digitized mammograms for this study. Eight features were calculated for each ROI. The importance of each feature in distinguishing masses from normal tissue was determined by stepwise linear discriminant analysis. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. We investigated the dependence of classification accuracy on the input features, and on the pixel distance and bit depth in the construction of the SGLD matrices. It was found that five of the texture features were important for the classification. The dependence of classification accuracy on distance and bit depth was weak for distances greater than 12 pixels and bit depths greater than seven bits. By randomly and equally dividing the data set into two groups, the classifier was trained and tested on independent data sets. The classifier achieved an average area under the ROC curve, Az, of 0.84 during training and 0.82 during testing. The results demonstrate the feasibility of using linear discriminant analysis in the texture feature space for classification of true and false detections of masses on mammograms in a computer-aided diagnosis scheme.

Presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized mammogram so that a simple edge detection algorithm can be used to define the boundaries of the objects. Once the object boundaries are known, morphological features are extracted and used by a classification algorithm to differentiate regions within the image. This paper introduces the DWCE algorithm and presents results of a preliminary study based on 25 digitized mammograms with biopsy proven masses. It also compares morphological feature classification based on sequential thresholding, linear discriminant analysis, and neural network classifiers for reduction of false-positive detections.

This paper presents segmentation and classification results of an automated algorithm for the detection of breast masses on digitized mammograms. Potential mass regions were first identified using density-weighted contrast enhancement (DWCE) segmentation applied to single-view mammograms. Once the potential mass regions had been identified, multiresolution texture features extracted from wavelet coefficients were calculated, and linear discriminant analysis (LDA) was used to classify the regions as breast masses or normal tissue. In this article the overall detection results for two independent sets of 84 mammograms used alternately for training and test were evaluated by free-response receiver operating characteristics (FROC) analysis. The test results indicate that this new algorithm produced approximately 4.4 false positive per image at a true positive detection rate of 90% and 2.3 false positives per image at a true positive rate of 80%.

We investigated the application of multiresolution global and local texture features to reduce false-positive detection in a computerized mass detection program. One hundred and sixty-eight digitized mammograms were randomly and equally divided into training and test groups. From these mammograms, two datasets were formed. The first dataset (manual) contained four regions of interest (ROIs) selected manually from each of the mammograms. One of the four ROIs contained a biopsy-proven mass and the other three contained normal parenchyma, including dense, mixed dense/fatty, and fatty tissues. The second dataset (hybrid) contained the manually extracted mass ROIs, along with normal tissue ROIs extracted by an automated Density-Weighted Contrast Enhancement (DWCE) algorithm as false-positive detections. A wavelet transform was used to decompose an ROI into several scales. Global texture features were derived from the low-pass coefficients in the wavelet transformed images. Local texture features were calculated from the suspicious object and the peripheral subregions. Linear discriminant models using effective features selected from the global, local, or combined feature spaces were established to maximize the separation between masses and normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classifier performance. The classification accuracy using global features were comparable to that using local features. With both global and local features, the average area, Az, under the test ROC curve, reached 0.92 for the manual dataset and 0.96 for the hybrid dataset, demonstrating statistically significant improvement over those obtained with global or local features alone. The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false-positive reduction.

We investigated a new approach to feature selection, and demonstrated its application in the task of differentiating regions of interest (ROIs) on mammograms as either mass or normal tissue. The classifier included a genetic algorithm (GA) for image feature selection, and a linear discriminant classifier or a backpropagation neural network (BPN) for formulation of the classifier outputs. The GA-based feature selection was guided by higher probabilities of survival for fitter combinations of features, where the fitness measure was the area Az under the receiver operating characteristic (ROC) curve. We studied the effect of different GA parameters on classification accuracy, and compared the results to those obtained with stepwise feature selection. The data set used in this study consisted of 168 ROIs containing biopsy-proven masses and 504 ROIs containing normal tissue. From each ROI, a total of 587 features were extracted, of which 572 were texture features and 15 were morphological features. The GA was trained and tested with several different partitionings of the ROIs into training and testing sets. With the best combination of the GA parameters, the average test Az value using a linear discriminant classifier reached 0.90, as compared to 0.89 for stepwise feature selection. Test Az values with a BPN classifier and a more limited feature pool were 0.90 with GA-based feature selection, and 0.89 for stepwise feature selection. The use of a GA in tailoring classifiers with specific design characteristics was also discussed. This study indicates that a GA can provide versatility in the design of linear or nonlinear classifiers without a trade-off in the effectiveness of the selected features.

Cycling of various cerebral metabolic substances, arterial vascular diameter, and flow has been noted by many workers at a frequency near 0.1 Hz. Suspicion that this phenomenon is dependent on the type of anesthesia led us to investigate the occurrence of cerebral blood flow (CBF) cycling with different anesthetics. Fifteen Sprague-Dawley rats were anesthetized with either pentobarbital (n = 5, 40-50 mg/kg), alpha-chloralose (n = 5, 60 mg/kg), or halothane (n = 5, 1-0.5%). Body temperature was maintained at 37 degrees C. Femoral arterial and venous catheters were placed, and a tracheotomy was performed, permitting artificial ventilation with 30% O2-70% N2. A closed cranial window was formed over a 3-mm diameter craniotomy. Mean arterial pressure (MABP), arterial partial pressures of CO2 and O2 (PaCO2 and PaO2), and pH were controlled and stabilized at normal values. CBF was determined using laser Doppler flowmetry. To induce cycling, MABP was transiently and repeatedly lowered by exsanguination. Fast Fourier analysis of selected 64-s flow recordings (n = 38) was performed. CBF cycling was observed, independent of the type of anesthesia, in all animals. In 36 epochs, cycling was induced when MABP was reduced to a mean pressure of 65 +/- 1.5 mmHg. The mean frequency and amplitude were 0.094 +/- 0.003 Hz and 6.6 +/- 0.5%, respectively. Cycling occurred without blood withdrawal in two epochs. With the use of the blood-withdrawal epochs (n = 36), all three anesthetics shared a common linear slope between amplitude and blood pressure (P < 0.02) and blood pressure change (P < 0.01). Pentobarbital differed from alpha-chloralose and halothane in the relation between cycling frequency and blood pressure. Only pentobarbital exhibited correlation between frequency and blood pressure (P < 0.02) and blood pressure change (P < 0.001). The occurrence of these oscillations is not related to the type of anesthesia, and they usually occur at MABP values that are near or just above the lower limit of autoregulation. At this pressure level, CBF oscillations would suggest that vasoconstrictive and dilatory forces are no longer in balance, but alternatively vying for control.

This paper presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive Density-Weighted Contrast Enhancement (DWCE) filter in conjunction with LaplacianGaussian (LG) edge detection. The new algorithm processes a mammogram in two stages. In the first stage the entire mammogram is filtered globally using a DWCE adaptive filter which enhances the local contrast of the image based on its local mean pixel values. The enhanced image is then segmented with an LG edge detector into isolated objects. In the second stage of processing, the DWCE adaptive filter and the edge detector are applied locally to each of the segmented object regions detected in the first stage. The number of objects is then reduced based on morphological features. ROIs are selected from the remaining object set based on the centroid locations of the individual objects. The selected ROTs are then input to either a linear discriminant analysis ( LDA) classifier or a convolution neural network (CNN) to further differentiate true-positives and false-positives. In this study ROIs obtained from a set of 84 images were used to train the LDA and CNN classifiers. The DWCE algorithm was then used to extract ROTs from a set of 84 test images. The trained LDA and CNN classifiers were subsequently applied to the extracted ROTs, and the dependence of the detection system's accuracy on the feature extraction and classification techniques was analyzed.