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The early detection of breast cancer: An ultrasonic approach using rf waveform analysis via pattern recognition
ABSTRACTS,
ULTRASONIC ItWGING AND TISSUE CHARACTERIZATION
SYMPOSIUM
classification rates of 72 to 80 percent for the canine and 56 to 63 percent for the human, but required more features to achieve these rates. Application of the nearest-neighbor rule resulted in higher rates of In the case of the correct classification for all the transforms used. canine samples, the identity transform performed best, achieving 91 percent correct classification with only 12 of an original 1024 features retained. For the human samples the DCT performed best with an 87 percent correctclassification rate and six of an original 1024 features retained. Research was supported in part by NASA contract NAS g-15452 and NIH Biomedical Sciences Support grant RR07114. THE EARLY DETECTION OF BREAST CANCER: AN ULTRASONIC APPROACH USING RF WAVEFORM ANALYSIS VIA PATTERN RECOGNITION, Morris S. GoodI, Joseph L. Rosel, and Barry B. Goldberg*, lDrexe1 University, Philadelphia, PA 19104 and *Thomas Jefferson University Hospital, Philadelphia, PA 19107. The purpose of this research effort is to define an accurate noninvasive technique for the diagnosis of malignant breast disease. Fluid areas were not of concern directly since present techniques were capable of Data collected for this distinguishing these areas by B-scan imaging. study, therefore, emphasize classification of solid tissue areas as either benign or malignant. Pattern recognition techniques such as the Fisher linear discriminant were used to determine various algorithms for classifying a tissue area. Tissue features used to develop the discriminant function were derived from the rf signal and evaluated by its Probability Distribution Function (PDF) for its decisive nature. Only features having a physical explanation to These its distribution and/or high discriminant value were retained. features were then used to develop an algorithm according to the pattern recognition method being implemented. Resulting algorithms were tested by the Jack-Knife technique to determine unbiased performance indices. Results from a 100 tissue area population provided sensitivity and specificity values of 95 percent and 67 percent respectively. ULTRASONIC TISSUE CHARACTERIZATION OF BREAST TUMORS Finettel, Alan RECOGNITION TECHNIQUES, Steven I. William Swindelll '* and Kai Haberl, lDepartment Arizona Health Sciences'Center and *Optical Sciences Center, Arizona, Tucson, AZ 85724
USING PATTERN R. Bleier*, of Radiology, University of
We consider the classification of in vivo human breast tumors as a problem in statistical pattern recognition. *% goal is the determination of accurate classification rules and confidence intervals for quantitative computer diagnosis of breast cancer using a conventional ultrasound imaging system. The basic approach is to measure properties of the (raw) backscattered RF waveform, and extract a small subset of information-rich features which can be used to distinguish between benign and malignant breast tumors. The data acquisition system consists of a commercially available ultrasonic imaging system (Octoson) interfaced to an RF amplifier with TGC and a Nicolet digital oscilloscope. A single-sideband receiver, under construction, will be used with a pulsed-Doppler transmitter and analog tape unit to acquire measures of spatial and temporal blood flow patterns associated with the tumor. Data from both systems are transmitted to a PDP 11/34 for offline processing. Feature extraction algorithms generate measurements related to image texture and Doppler related features. Over 150 features can be generated for each digitized A-scan. This high dimensional measurement space is then reduced to a subspace containing features whose information content iS sufficient to allow an accurate two-way classification of breast tumors
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ULTRASONIC ItWGING AND TISSUE CHARACTERIZATION
SYMPOSIUM
classification rates of 72 to 80 percent for the canine and 56 to 63 percent for the human, but required more features to achieve these rates. Application of the nearest-neighbor rule resulted in higher rates of In the case of the correct classification for all the transforms used. canine samples, the identity transform performed best, achieving 91 percent correct classification with only 12 of an original 1024 features retained. For the human samples the DCT performed best with an 87 percent correctclassification rate and six of an original 1024 features retained. Research was supported in part by NASA contract NAS g-15452 and NIH Biomedical Sciences Support grant RR07114. THE EARLY DETECTION OF BREAST CANCER: AN ULTRASONIC APPROACH USING RF WAVEFORM ANALYSIS VIA PATTERN RECOGNITION, Morris S. GoodI, Joseph L. Rosel, and Barry B. Goldberg*, lDrexe1 University, Philadelphia, PA 19104 and *Thomas Jefferson University Hospital, Philadelphia, PA 19107. The purpose of this research effort is to define an accurate noninvasive technique for the diagnosis of malignant breast disease. Fluid areas were not of concern directly since present techniques were capable of Data collected for this distinguishing these areas by B-scan imaging. study, therefore, emphasize classification of solid tissue areas as either benign or malignant. Pattern recognition techniques such as the Fisher linear discriminant were used to determine various algorithms for classifying a tissue area. Tissue features used to develop the discriminant function were derived from the rf signal and evaluated by its Probability Distribution Function (PDF) for its decisive nature. Only features having a physical explanation to These its distribution and/or high discriminant value were retained. features were then used to develop an algorithm according to the pattern recognition method being implemented. Resulting algorithms were tested by the Jack-Knife technique to determine unbiased performance indices. Results from a 100 tissue area population provided sensitivity and specificity values of 95 percent and 67 percent respectively. ULTRASONIC TISSUE CHARACTERIZATION OF BREAST TUMORS Finettel, Alan RECOGNITION TECHNIQUES, Steven I. William Swindelll '* and Kai Haberl, lDepartment Arizona Health Sciences'Center and *Optical Sciences Center, Arizona, Tucson, AZ 85724
USING PATTERN R. Bleier*, of Radiology, University of
We consider the classification of in vivo human breast tumors as a problem in statistical pattern recognition. *% goal is the determination of accurate classification rules and confidence intervals for quantitative computer diagnosis of breast cancer using a conventional ultrasound imaging system. The basic approach is to measure properties of the (raw) backscattered RF waveform, and extract a small subset of information-rich features which can be used to distinguish between benign and malignant breast tumors. The data acquisition system consists of a commercially available ultrasonic imaging system (Octoson) interfaced to an RF amplifier with TGC and a Nicolet digital oscilloscope. A single-sideband receiver, under construction, will be used with a pulsed-Doppler transmitter and analog tape unit to acquire measures of spatial and temporal blood flow patterns associated with the tumor. Data from both systems are transmitted to a PDP 11/34 for offline processing. Feature extraction algorithms generate measurements related to image texture and Doppler related features. Over 150 features can be generated for each digitized A-scan. This high dimensional measurement space is then reduced to a subspace containing features whose information content iS sufficient to allow an accurate two-way classification of breast tumors
208