Design and evaluation of an external filter technique for exposure equalization in mammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134768/1/mp8659.pd

Classification of compressed breast shapes for the design of equalization filters in xâ ray mammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134971/1/mp8272.pd

Computerized image analysis: Estimation of breast density on mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134888/1/mp6640.pd

Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis

A genetic algorithm (GA) based feature selection method was developed for the design of high-sensitivity classifiers, which were tailored to yield high sensitivity with high specificity. The fitness function of the GA was based on the receiver operating characteristic (ROC) partial area index, which is defined as the average specificity above a given sensitivity threshold. The designed GA evolved towards the selection of feature combinations which yielded high specificity in the high-sensitivity region of the ROC curve, regardless of the performance at low sensitivity. This is a desirable quality of a classifier used for breast lesion characterization, since the focus in breast lesion characterization is to diagnose correctly as many benign lesions as possible without missing malignancies. The high-sensitivity classifier, formulated as the Fisher's linear discriminant using GA-selected feature variables, was employed to classify 255 biopsy-proven mammographic masses as malignant or benign. The mammograms were digitized at a pixel size of mm, and regions of interest (ROIs) containing the biopsied masses were extracted by an experienced radiologist. A recently developed image transformation technique, referred to as the rubber-band straightening transform, was applied to the ROIs. Texture features extracted from the spatial grey-level dependence and run-length statistics matrices of the transformed ROIs were used to distinguish malignant and benign masses. The classification accuracy of the high-sensitivity classifier was compared with that of linear discriminant analysis with stepwise feature selection . With proper GA training, the ROC partial area of the high-sensitivity classifier above a true-positive fraction of 0.95 was significantly larger than that of , although the latter provided a higher total area under the ROC curve. By setting an appropriate decision threshold, the high-sensitivity classifier and correctly identified 61% and 34% of the benign masses respectively without missing any malignant masses. Our results show that the choice of the feature selection technique is important in computer-aided diagnosis, and that the GA may be a useful tool for designing classifiers for lesion characterization.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48962/2/m81014.pd

Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis

Computer-aided diagnosis schemes are being developed to assist radiologists in mammographic interpretation. In this study, the authors investigated whether texture features could be used to distinguish between mass and non-mass regions in clinical mammograms. Forty-five regions of interest (ROIs) containing true masses with various degrees of visibility and 135 ROIs containing normal breast parenchyma were extracted manually from digitized mammograms as case samples. Spatial-grey-level-dependence (SGLD) matrices of each ROI were calculated and eight texture features were calculated from the SGLD matrices. The correlation and class-distance properties of extracted texture features were analysed. Selected texture features were input into a modified decision-tree classification scheme. The performance of the classifier was evaluated for different feature combinations and orders of features on the tree. A classification accuracy of about 89% sensitivity and 76% specificity was obtained for ordered features, sum average, correlation, and energy, during the training procedure. With a leave-one-out method, the test result was about 76% sensitivity and 64% specificity. The results of this preliminary study demonstrate the feasibility of using texture information for classification of mass and normal breast tissue, which will be likely to be useful for classifying true and false detections in computer-aided diagnosis programmes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48958/2/pb941210.pd

ROC study of the effect of stereoscopic imaging on assessment of breast lesions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135047/1/mp0172.pd

Automated Ultrasound Scanning on a Dual‐Modality Breast Imaging System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135219/1/jum2007265645.pd

An observer study comparing spot imaging regions selected by radiologists and a computer for an automated stereo spot mammography technique

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134832/1/mp7492.pd

Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space

The authors 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. The authors 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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48960/2/pb950510.pd

Computerized nipple identification for multiple image analysis in computerâ aided diagnosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135606/1/mp0713.pd