Reza Momenan

Pattern recognition methods for optimizing multivariate tissue signatures in diagnostic ultrasound

Described is a supervised parametric approach to the detection and classification of disease from acoustic data. Statistical pattern recognition techniques are implemented to design the best ultrasonic tissue signature from a set of measurements and for a given task, and to rate its performance in a way that can be compared with other diagnostic tools. In this paper, we considered combinations of four ultrasonic tissue parameters to discriminate, in vivo, between normal liver and chronic active hepatitis. The separation between normal and diseased samples was made by application of the Bayes decision rule for minimum risk which includes the prior probability for the presence of disease and the cost of misclassification. Large differences in classification performance of various tissue parameter combinations were demonstrated using the Hotelling trace criterion (HTC) and receiver operating characteristic (ROC) analysis. The ability of additional measurements to increase or decrease discriminability, even measurements from other diagnostic modalities, can be evaluated directly in this manner.

Application of pattern recognition techniques in ultrasound tissue characterization

An approach for the application of multivariate pattern recognition techniques to detection of diffuse and focal disease using acoustic data is reviewed. Supervised and unsupervised techniques are implemented to design the best ultrasonic tissue signature for a given task from a set of measurements. The performances of both techniques are evaluated and compared using several methods. However, it is desirable to utilize a technique that quantitatively detects and displays the heterogeneity of an ultrasound image. It is shown that, for a particular task, choosing features with physical significance will make the classification of the data more robust. It is also shown that the success of combining supervised and unsupervised techniques using such features extends beyond discrimination of one class of data from the other and that the approach can be used to grade and the variations in the same tissue type.<<ETX>>

Characterization of tissue from ultrasound images

The application of a procedure for classifying tissue types from unlabeled acoustic measurements using unsupervised analysis is reviewed and evaluated. Unsupervised learning techniques are applied to the problems of detecting tumors within an organ and of discriminating between tissue types of two neighboring organs such as the liver and the kidney.<<ETX>>

Supervised Pattern Recognition Techniques In Quantitative Diagnostic Ultrasound

The methods of statistical pattern recognition are well suited to the problems of in vivo ultrasonic tissue characterization. This paper describes supervised pattern recognition methods for selecting features for tissue classification, calculating decision boundaries within the selected feature space, and evaluating the performance. We address the considerations of dimensionality and feature size which are important in classification problems where the underlying probability distributions are not completely known. Examples are given for the detection of diffuse liver disease in the clinical environment.

Quantitative Ultrasonic Imaging

Currently ultrasonic B-scan images combine many acoustic properties and imaging system parameters to form the image. It may, however, be advantageous to image acoustic properties individually or in select groups to obtain a more direct interpretation of the results. This report describes two methods of quantitative ultrasonic imaging which we are pursuing. The two methods are: single-feature parametric imaging and multi-feature tissue-type imaging. Trade-offs among contrast, noise, and spatial resolution using computer simulations and phantom measurements are discussed.

Application Of Cluster Analysis And Unsupervised Learning To Multivariate Tissue Characterization

This paper describes a procedure for classifying tissue types from unlabeled acoustic measurements (data type unknown) using unsupervised cluster analysis. These techniques are being applied to unsupervised ultrasonic image segmentation and tissue characteriza-tion. The performance of a new clustering technique is measured and compared with supervised methods, such as a linear Bayes classifier. In these comparisons two objectives are sought: a) How well does the clustering method group the data? b) Do the clusters correspond to known tissue classes? The first question is investigated by a measure of cluster similarity and dispersion. The second question involves a comparison with a supervised technique using labeled data.

Application of statistical pattern recognition and image processing in ultrasound tissue characterization

This paper consists of two parts. The first part considers the limitations imposed by statistical properties of ultrasound images. Through this analysis the minimum detectable tumor size from an ultrasound Bscan using the current state of the art is determined the second part describes an improvement to a successful tissue-characterization algorithm that adds several image processing steps to compute the tissuecharacterization features. The inclusion of such steps will enable the tissue-characterization algorithm to take advantage of visual cues similar to those that a clinician would use to differentiate various organs and segments of the image. This in turn expands the applicability of the present tissue-characterization algorithm from multivariate to multiorgan and multidisease cases.