BACKGROUND: Identification of dark areas inside a melanocytic lesion (ML) is of great importance for melanoma diagnosis, both during clinical examination and employing programs for automated image analysis. OBJECTIVE: The aim of our study was to compare two different methods for the automated identification and description of dark areas in epiluminescence microscopy images of MLs and to evaluate their diagnostic capability. METHODS: Two methods for the automated extraction of 'absolute' (ADAs) and 'relative' dark areas (RDAs) and a set of parameters for their description were developed and tested on 339 images of MLs acquired by means of a polarized-light videomicroscope. RESULTS: Significant differences in dark area distribution between melanomas and nevi were observed employing both methods, permitting a good discrimination of MLs (diagnostic accuracy = 74.6 and 71.2% for ADAs and RDAs, respectively). CONCLUSIONS: Both methods for the automated identification of dark areas are useful for melanoma diagnosis and can be implemented in programs for image analysis. Copyright 2004 S. Karger AG, Basel
BACKGROUND: Identification of dark areas inside a melanocytic lesion (ML) is of great importance for melanoma diagnosis, both during clinical examination and employing programs for automated image analysis. OBJECTIVE: The aim of our study was to compare two different methods for the automated identification and description of dark areas in epiluminescence microscopy images of MLs and to evaluate their diagnostic capability. METHODS: Two methods for the automated extraction of 'absolute' (ADAs) and 'relative' dark areas (RDAs) and a set of parameters for their description were developed and tested on 339 images of MLs acquired by means of a polarized-light videomicroscope. RESULTS: Significant differences in dark area distribution between melanomas and nevi were observed employing both methods, permitting a good discrimination of MLs (diagnostic accuracy = 74.6 and 71.2% for ADAs and RDAs, respectively). CONCLUSIONS: Both methods for the automated identification of dark areas are useful for melanoma diagnosis and can be implemented in programs for image analysis. Copyright 2004 S. Karger AG, Basel
Authors: M Emre Celebi; Hitoshi Iyatomi; William V Stoecker; Randy H Moss; Harold S Rabinovitz; Giuseppe Argenziano; H Peter Soyer Journal: Comput Med Imaging Graph Date: 2008-09-19 Impact factor: 4.790
Authors: William V Stoecker; Kapil Gupta; Bijaya Shrestha; Mark Wronkiewiecz; Raeed Chowdhury; R Joe Stanley; Jin Xu; Randy H Moss; M Emre Celebi; Harold S Rabinovitz; Margarat Oliviero; Joseph M Malters; Isabel Kolm Journal: Skin Res Technol Date: 2009-08 Impact factor: 2.365
Authors: Azmath Khan; Kapil Gupta; R J Stanley; William V Stoecker; Randy H Moss; Giuseppe Argenziano; H Peter Soyer; Harold S Rabinovitz; Armand B Cognetta Journal: Comput Med Imaging Graph Date: 2008-11-21 Impact factor: 4.790
Authors: Lavinia Ferrante di Ruffano; Yemisi Takwoingi; Jacqueline Dinnes; Naomi Chuchu; Susan E Bayliss; Clare Davenport; Rubeta N Matin; Kathie Godfrey; Colette O'Sullivan; Abha Gulati; Sue Ann Chan; Alana Durack; Susan O'Connell; Matthew D Gardiner; Jeffrey Bamber; Jonathan J Deeks; Hywel C Williams Journal: Cochrane Database Syst Rev Date: 2018-12-04