title: Prostate tissue characterization/classification in 144 patient population using wavelet and higher order spectra features from transrectal ultrasound images.
abstract: 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.
authors.affiliation: Section of Minimally Invasive Urologic Surgery, The Warren Alpert Medical School of Brown University, Providence, RI 02905. vinitha.sree@gmail.com.
authors.forename: Gyan
authors.initials: G
authors.lastname: Pareek
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: S Vinitha
authors.initials: SV
authors.lastname: Sree
authors.forename: G
authors.initials: G
authors.lastname: Swapna
authors.forename: Ratna
authors.initials: R
authors.lastname: Yantri
authors.forename: Roshan Joy
authors.initials: RJ
authors.lastname: Martis
authors.forename: Luca
authors.initials: L
authors.lastname: Saba
authors.forename: Ganapathy
authors.initials: G
authors.lastname: Krishnamurthi
authors.forename: Giorgio
authors.initials: G
authors.lastname: Mallarini
authors.forename: Ayman
authors.initials: A
authors.lastname: El-Baz
authors.forename: Shadi
authors.initials: S
authors.lastname: Al Ekish
authors.forename: Michael
authors.initials: M
authors.lastname: Beland
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D011467:Prostate / Q000000981:diagnostic imaging* / Q000473:pathology; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012007:Rectum / Q000000981:diagnostic imaging; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014463:Ultrasonography