Application of Pattern Recognition Techniques to Breast Cancer Detection: Ultrasonic Analysis of 100 Pathologically Confirmed Tissue Areas

1982 ◽  
Vol 4 (4) ◽  
pp. 378-396 ◽  
Author(s):  
Morris S. Good ◽  
Joseph L. Rose ◽  
Barry B. Goldberg
Keyword(s):  
Pattern Recognition ◽  
Ultrasonic Pulse ◽  
Waveform Analysis ◽  
Algorithm Performance ◽  
Linear Discriminant ◽  
Pattern Recognition Techniques ◽  
Pattern Recognition Methods ◽  
Jackknife Procedure ◽  
Pulse Echo

Ultrasonic pulse-echo rf waveform analysis and selected pattern recognition methods were applied to classification of breast tissue. Emphasis was placed on the classification of solid tissue areas since fluid areas are easily identified by present B-scan techniques. Pattern recognition techniques such as the Fisher Linear Discriminant (FLD), Probability Density Function (PDF) curves, jackknife estimate and committee vote were used to construct and evaluate a two class algorithm, malignant versus benign tissue areas. A data base consisting of frequency domain features from 100 pathologically confirmed tissue areas from 87 patients were used to train the algorithm. Algorithm performance was acquired via the generalized jackknife procedure to significantly reduce the bias frequently encountered in algorithm evaluation. Estimated values of algorithm performance are sensitivity and specificity values of 96 percent and 68 percent, respectively.

scholarly journals The application of pattern recognition techniques in metabolite fingerprinting of six differentPhyllanthusspp.

2011 ◽  
Vol 26 (1) ◽  
pp. 69-78 ◽  
Author(s):  
Saravanan Dharmaraj ◽  
Lay-Harn Gam ◽  
Shaida Fariza Sulaiman ◽  
Sharif Mahsufi Mansor ◽  
Zhari Ismail
Keyword(s):  
Pattern Recognition ◽  
Principal Component ◽  
Canonical Variate ◽  
Batch Mode ◽  
Mid Infrared ◽  
Linear Discriminant ◽  
Pattern Recognition Techniques ◽  
Pattern Recognition Methods ◽  
Ftir Spectrometer ◽  
Component Models

FTIR spectroscopy was used together with multivariate analysis to distinguish six different species ofPhyllanthus. Among these speciesP. niruri,P. debilisandP. urinariaare morphologically similar whereasP. acidus,P. emblicaandP. myrtifoliusare different. The FTIR spectrometer was used to obtain the mid-infrared spectra of the dried powdered leaves in the region of 400–4000 cm−1. The region of 400–2000 cm−1was analyzed with four different pattern recognition methods. Initially, principal component analysis (PCA) was used to reduce the spectra to six principal components and these variables were used for linear discriminant analysis (LDA). The second technique used LDA on most discriminating wavenumber variables as searched by genetic algorithm using canonical variate approach for either 30 or 60 generations. SIMCA, which consisted of constructing an enclosure for each species using separate principal component models, was the third technique. Finally, multi-layer neural network with batch mode of backpropagation learning was used to classify the samples. The best results were obtained with GA of 60 gens. When LDA was run with the six wavenumbers chosen (1151, 1578, 1134, 609, 876 and 1227), 100% of the calibration spectra and 96.3% of the validation spectra were correctly assigned.

scholarly journals Walk–run classification of symmetrical gaits in the horse: a multidimensional approach

2008 ◽  
Vol 6 (33) ◽  
pp. 335-342 ◽  
Author(s):  
Sandra D Starke ◽  
Justine J Robilliard ◽  
Renate Weller ◽  
Alan M Wilson ◽  
Thilo Pfau
Keyword(s):  
Pattern Recognition ◽  
Mechanical Energy ◽  
Reaction Force ◽  
Duty Factor ◽  
Vertical Ground Reaction Force ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Force Profile ◽  
Icelandic Horses

Walking and running are two mechanisms for minimizing energy expenditure during terrestrial locomotion. Duty factor, dimensionless speed, existence of an aerial phase, percentage recovery (PR) or phase shift of mechanical energy and shape of the vertical ground reaction force profile have been used to discriminate between walking and running. Although these criteria work well for the classification of most quadrupedal gaits, they result in conflicting evidence for some gaits, such as the tölt (a symmetrical, four-beat gait used by Icelandic horses). We use established pattern recognition methods to test the hypothesis that the tölt is a running gait based on an automated and optimized decision drawn from four features (dimensionless speed, duty factor, length of aerial phase and PR for over 6000 strides from four symmetrical gaits in seven Icelandic horses) simultaneously. We compare this decision with the use of each of these features in isolation. Sensitivity and specificity values were used to determine optimal thresholds for classifying tölt strides based on each feature separately. Duty factor and dimensionless speed indicate that tölt is more similar to running, while aerial phase and PR indicate that it is more similar to walking. Then, two multidimensional pattern recognition approaches, multivariate Gaussian densities and linear discriminant analysis, were used and it was shown that, in terms of stochastic multidimensional discrimination, tölt is more similar to ‘running’. The approaches presented here have potential to be extended to studies on similar ‘ambling’ gaits in other quadrupeds.

Motor Oil Classification Using Color Histograms and Pattern Recognition Techniques

2018 ◽  
Vol 101 (6) ◽  
pp. 1967-1976 ◽  
Author(s):  
Shiva Ahmadi ◽  
Ahmad Mani-Varnosfaderani ◽  
Biuck Habibi
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Classification Problem ◽  
Classification Model ◽  
Motor Oil ◽  
Pattern Recognition Techniques ◽  
Color Histograms ◽  
Different Types ◽  
Motor Oils

Abstract Motor oil classification is important for quality control and the identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

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