Literature DB >> 14654152

An automatic approach for morphological analysis and malignancy evaluation of ovarian masses using B-scans.

Yair Zimmer1, Ron Tepper, Solange Akselrod.   

Abstract

Ovarian masses are a common phenomenon among women of all ages. The importance of prompt diagnosis of ovarian malignancies is obvious, due to the high mortality rate and the difficulty to detect a tumor in its early stages. In this work, an automatic technique for quantitative analysis and malignancy detection of ovarian masses using B-scan ultrasound (US) images is presented. The core of the technique is morphologic analysis of the ovarian mass. The method employed for this task is divided into two major stages: initial classification of the mass (into one of the three major tumor types: cyst, semisolid, solid), and detailed analysis of the mass. Malignancy evaluation is performed based on the collected data and the criteria provided by commonly used scoring systems. The results reflect adequate performance of the automatic method developed (referring to clinical requirements).

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Year:  2003        PMID: 14654152     DOI: 10.1016/j.ultrasmedbio.2003.08.013

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  5 in total

1.  Ovarian tumor characterization and classification using ultrasound-a new online paradigm.

Authors:  U Rajendra Acharya; S Vinitha Sree; Luca Saba; Filippo Molinari; Stefano Guerriero; Jasjit S Suri
Journal:  J Digit Imaging       Date:  2013-06       Impact factor: 4.056

2.  A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study.

Authors:  Olivier Lucidarme; Jean-Paul Akakpo; Seth Granberg; Mario Sideri; Hanoch Levavi; Achim Schneider; Philippe Autier; Dror Nir; Harry Bleiberg
Journal:  Eur Radiol       Date:  2010-03-20       Impact factor: 5.315

3.  Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.

Authors:  S Khazendar; A Sayasneh; H Al-Assam; H Du; J Kaijser; L Ferrara; D Timmerman; S Jassim; T Bourne
Journal:  Facts Views Vis Obgyn       Date:  2015

4.  GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization.

Authors:  U Rajendra Acharya; S Vinitha Sree; Sanjeev Kulshreshtha; Filippo Molinari; Joel En Wei Koh; Luca Saba; Jasjit S Suri
Journal:  Technol Cancer Res Treat       Date:  2013-12-06

5.  A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125.

Authors:  Valentina Chiappa; Matteo Interlenghi; Giorgio Bogani; Christian Salvatore; Francesca Bertolina; Giuseppe Sarpietro; Mauro Signorelli; Dominique Ronzulli; Isabella Castiglioni; Francesco Raspagliesi
Journal:  Eur Radiol Exp       Date:  2021-07-26
  5 in total

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