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Ganesan K.,Ngee Ann Polytechnic | Acharya U.R.,Ngee Ann Polytechnic | Acharya U.R.,University of Malaya | Chua C.K.,Ngee Ann Polytechnic | And 2 more authors.
IEEE Transactions on Instrumentation and Measurement | Year: 2014

Mammography is one of the first diagnostic tests to prescreen breast cancer. Early detection of breast cancer has been known to improve recovery rates to a great extent. In most medical centers, experienced radiologists are given the responsibility of analyzing mammograms. But, there is always a possibility of human error. Errors can frequently occur as a result of fatigue of the observer, resulting in interobserver and intraobserver variations. The sensitivity of mammographic screening also varies with image quality. To offset different kinds of variability and to standardize diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This paper presents a one-class classification pipeline for the classification of breast cancer images into benign and malignant classes. Because of the sparse distribution of abnormal mammograms, the two-class classification problem is reduced to a one-class outlier identification problem. Trace transform, which is a generalization of the Radon transform, has been used to extract the features. Several new functionals specific to mammographic image analysis have been developed and implemented to yield clinically significant features. Classifiers such as the linear discriminant classifier, quadratic discriminant classifier, nearest mean classifier, support vector machine, and the Gaussian mixture model (GMM) were used. For automated diagnosis, the classification pipeline was tested on a set of 313 mammograms provided by the Singapore Anti-Tuberculosis Association CommHealth. A maximum accuracy rate of 92.48% has been obtained using GMMs. © 2013 IEEE. Source


Tan J.H.,Ngee Ann Polytechnic | Acharya U.R.,Ngee Ann Polytechnic | Tan C.,SATA CommHealth | Abraham K.T.,SATA CommHealth | Lim C.M.,Ngee Ann Polytechnic
Journal of Medical Systems | Year: 2012

Textural properties of normal and tuberculosis posterior-anterior chest radiographs were looked into in this investigation. The proposed computerized scheme segmented the lung field of interest using a user-guided snake algorithm and extracted the corresponding pixel data. For both normal and tuberculosis radiographs, the grayscale intensity distribution within the region of interest was analyzed to study their respective characteristics, and fed to classifiers for automated classification. Statistically the tuberculosis infected radiographs manifested a higher variance, third moment, entropy and a lower mean value in their intensity distributions, compared to their normal peers. The greater disparities between a particular radiograph and the confidence interval determined by our normal groups on some of the features were observed to be related to the level of haziness at the upper lobe. Lastly, the C4.5 (a decision tree based classifier)-adaboost achieved an accuracy of 94.9% in normal-tuberculosis classification. An integrated index, called tuberculosis index (TI), is proposed based on texture features to discriminate normal and tuberculosis chest radiographs using just one index or number. We hope this TI can be used as an adjunct tool by the radiographers in their daily screening. © 2011 Springer Science+Business Media, LLC. Source


Ganesan K.,Ngee Ann Polytechnic | Acharya R.U.,Ngee Ann Polytechnic | Acharya R.U.,University of Malaya | Chua C.K.,Ngee Ann Polytechnic | And 3 more authors.
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine | Year: 2013

Mammograms are by far one of the most preferred methods of screening for breast cancer. Early detection of breast cancer can improve survival rates to a greater extent. Although the analysis and diagnosis of breast cancer are done by experienced radiologists, there is always the possibility of human error. Interobserver and intraobserver errors occur frequently in the analysis of medical images, given the high variability between every patient. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. This article presents a classification pipeline to improve the accuracy of differentiation between normal, benign, and malignant mammograms. Several features based on higher-order spectra, local binary pattern, Laws' texture energy, and discrete wavelet transform were extracted from mammograms. Feature selection techniques based on sequential forward, backward, plus-l-takeaway-r, individual, and branch-and-bound selections using the Mahalanobis distance criterion were used to rank the features and find classification accuracies for combination of several features based on the ranking. Six classifiers were used, namely, decision tree classifier, fisher classifier, linear discriminant classifier, nearest mean classifier, Parzen classifier, and support vector machine classifier. We evaluated our proposed methodology with 300 mammograms obtained from the Digital Database for Screening Mammography and 300 mammograms from the Singapore Anti-Tuberculosis Association CommHealth database. Sensitivity, specificity, and accuracy values were used to compare the performances of the classifiers. Our results show that the decision tree classifier demonstrated an excellent performance compared to other classifiers with classification accuracy, sensitivity, and specificity of 91% for the Digital Database for Screening Mammography database and 96.8% for the Singapore Anti-Tuberculosis Association CommHealth database. © IMechE 2013. Source

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