Evaluation of traumatic groin arteriovenous fistulas with duplex Doppler sonography.

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135191/1/jum19898121.pd

Sonographic features of breast hamartomas.

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135365/1/jum19909285.pd

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

Discrimination of sonographically detected breast masses using frequency shift color Doppler imaging in combination with age and gray scale criteria.

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135358/1/jum2001204343.pd

Automated pectoral muscle identification on MLOâ view mammograms: Comparison of deep neural network to conventional computer vision

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/1/mp13451_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/2/mp13451.pd

Computerâ aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134897/1/mp2072.pd

Computer aided detection of clusters of microcalcifications on full field digital mammograms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134903/1/mp1710.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

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

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