Doppler ultrasound color flow imaging in the study of breast cancer: Preliminary findings

A prospective study of the Doppler color flow features of 55 proved breast cancers was performed. On a three-level scale of low to marked vascularity, visual assessment of the color flow images classified 82% of the cancers as moderately or markedly vascular (minimal: 14%, moderate: 29%, marked: 53%). Four percent of the cancers had no detectable flow. In 29 women, a volume of tissue comparable to the cancer was scanned in the contralateral normal breast. Sixty-nine percent of the normal breasts had moderate or marked vascularity (minimal: 28%, moderate: 41%, marked: 28%), and 3% were avascular. There was poor distinction between normal tissues and cancer which suggests that more sensitive Doppler methods than were employed in this study may be needed in order to detect the small vessel flow reported to be rather specific for malignancy. The high, 82%, detection rate of tumor vessels in this study suggests the potential use of color flow Doppler for directing more specific but lengthy Doppler procedures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28897/1/0000734.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

Case report 220

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46818/1/256_2004_Article_BF00352557.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 classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network

We investigated the feasibility of using texture features extracted from mammograms to predict whether the presence of microcalcifications is associated with malignant or benign pathology. Eighty-six mammograms from 54 cases (26 benign and 28 malignant) were used as case samples. All lesions had been recommended for surgical biopsy by specialists in breast imaging. A region of interest (ROI) containing the microcalcifications was first corrected for the low-frequency background density variation. Spatial grey level dependence (SGLD) matrices at ten different pixel distances in both the axial and diagonal directions were constructed from the background-corrected ROI. Thirteen texture measures were extracted from each SGLD matrix. Using a stepwise feature selection technique, which maximized the separation of the two class distributions, subsets of texture features were selected from the multi-dimensional feature space. A backpropagation artificial neural network (ANN) classifier was trained and tested with a leave-one-case-out method to recognize the malignant or benign microcalcification clusters. The performance of the ANN was analysed with receiver operating characteristic (ROC) methodology. It was found that a subset of six texture features provided the highest classification accuracy among the feature sets studied. The ANN classifier achieved an area under the ROC curve of 0.88. By setting an appropriate decision threshold, 11 of the 28 benign cases were correctly identified (39% specificity) without missing any malignant cases (100% sensitivity) for patients who had undergone biopsy. This preliminary result indicates that computerized texture analysis can extract mammographic information that is not apparent by visual inspection. The computer-extracted texture information may be used to assist in mammographic interpretation, with the potential to reduce biopsies of benign cases and improve the positive predictive value of mammography.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48961/2/m70308.pd

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134952/1/mp8389.pd

Falseâ positive reduction technique for detection of masses on digital mammograms: Global and local multiresolution texture analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135042/1/mp8011.pd

Sonographic Spectrum of Focal Splenic Lesions

Sonograms of 50 patients with a clinical suspicion of splenic abnormality were reviewed. With the use of high-resolution static and real-time equipment, combined with variable patient positioning, focal splenic lesions were well imaged. A wide variety of pathologic entities was sonographically demonstrated, including traumatic hematomas, primary and metastatic neoplasms, cysts, infarcts, and inflammatory granulomas.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66713/2/10.1177_875647938600200602.pd

Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134857/1/mp7756.pd