Prostate Tissue Characterization/Classification in 144 Patient Population Using Wavelet and Higher Order Spectra Features from Transrectal Ultrasound Images
| Autor: | S. Vinitha Sree, Ayman El-Baz, Luca Saba, Giorgio Mallarini, Shadi Al Ekish, Ratna Yantri, U. Rajendra Acharya, Michael D. Beland, Jasjit S. Suri, G. Swapna, Gyan Pareek, Roshan Joy Martis, Ganapathy Krishnamurthi |
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| Rok vydání: | 2013 |
| Předmět: |
Adult
Male Discrete wavelet transform Cancer Research Pathology medicine.medical_specialty Support Vector Machine Sensitivity and Specificity Wavelet Image Interpretation Computer-Assisted medicine Humans Aged Retrospective Studies Ultrasonography Aged 80 and over Ground truth Standard test image business.industry Ultrasound Prostate Rectum Prostatic Neoplasms Pattern recognition Tissue characterization Middle Aged Support vector machine Oncology Artificial intelligence business Classifier (UML) Algorithms |
| Zdroj: | Technology in Cancer Research & Treatment. 12:545-557 |
| ISSN: | 1533-0338 1533-0346 |
| DOI: | 10.7785/tcrt.2012.500346 |
| Popis: | In this work, we have proposed an on-line computer-aided diagnostic system called “UroImage” that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based feature and four HOS-based features) are extracted from the test image. These on-line features are transformed by the classifier parameters obtained using the training dataset to determine the class. We trained and tested six classifiers. The dataset used for evaluation had 144 TRUS images which were split into training and testing sets. Three-fold and ten-fold cross-validation protocols were adopted for training and estimating the accuracy of the classifiers. The ground truth used for training was obtained using the biopsy results. Among the six classifiers, using 10-fold cross-validation technique, Support Vector Machine and Fuzzy Sugeno classifiers presented the best classification accuracy of 97.9% with equally high values for sensitivity, specificity and positive predictive value. Our proposed automated system, which achieved more than 95% values for all the performance measures, can be an adjunct tool to provide an initial diagnosis for the identification of patients with prostate cancer. The technique, however, is limited by the limitations of 2D ultrasound guided biopsy, and we intend to improve our technique by using 3D TRUS images in the future. |
| Databáze: | OpenAIRE |
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