title: Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach.
abstract: The discrimination between benign and malignant adnexal masses in ultrasound images represents one of the most challenging problems in gynecologic practice. In the study described here, a new method for automatic discrimination of adnexal masses based on a neural networks approach was tested. The proposed method first calculates seven different types of characteristics (local binary pattern, fractal dimension, entropy, invariant moments, gray level co-occurrence matrix, law texture energy and Gabor wavelet) from ultrasound images of the ovary, from which several features are extracted and collected together with the clinical patient age. The proposed technique was validated using 106 benign and 39 malignant images obtained from 145 patients, corresponding to its probability of appearance in general population. On evaluation of the classifier, an accuracy of 98.78%, sensitivity of 98.50%, specificity of 98.90% and area under the curve of 0.997 were calculated.
authors.affiliation: Department of Electric and Electronic Engineering, Public University of Navarra, Pamplona, Spain. Electronic address: veronica.aramendia@gmail.com.
authors.forename: Verónica
authors.initials: V
authors.lastname: Aramendía-Vidaurreta
authors.affiliation: Department of Electric and Electronic Engineering, Public University of Navarra, Pamplona, Spain.
authors.forename: Rafael
authors.initials: R
authors.lastname: Cabeza
authors.affiliation: Department of Electric and Electronic Engineering, Public University of Navarra, Pamplona, Spain.
authors.forename: Arantxa
authors.initials: A
authors.lastname: Villanueva
authors.affiliation: Department of Electric and Electronic Engineering, Public University of Navarra, Pamplona, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: Navallas
authors.affiliation: Department of Obstetrics and Gynecology, Clinical University of Navarra, Pamplona, Spain.
authors.forename: Juan Luis
authors.initials: JL
authors.lastname: Alcázar
mesh_terms: D000291:Adnexal Diseases / Q000000981:diagnostic imaging*; D000328:Adult; D000465:Algorithms; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography
keywords: Adnexal mass; Classification; Neural network; Texture feature