Literature DB >> 1623493

Texture in skin images: comparison of three methods to determine smoothness.

W V Stoecker1, C S Chiang, R H Moss.   

Abstract

Smooth texture, a critical feature in skin tumor diagnosis, is analyzed using three texture measurement methods. A dermatologist classified 1290 small blocks within 42 tumor images as smooth, partially smooth, or nonsmooth. Texture discriminatory power of three methods were compared: the neighboring gray-level dependence matrix (NGLDM) method of Sun and Wee, the circular symmetric autoregressive random field model of Kashyap and Khotanzad, and a new peak-variance method. The texture analysis method that allows best prediction of smoothness for our tumor domain is the NGLDM method, affording 98% correct prediction of a smooth block with 21% false positives. We discuss applicability of texture analysis to dermatology.

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Year:  1992        PMID: 1623493     DOI: 10.1016/0895-6111(92)90072-h

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  11 in total

1.  Detection of pigment network in dermatoscopy images using texture analysis.

Authors:  Murali Anantha; Randy H Moss; William V Stoecker
Journal:  Comput Med Imaging Graph       Date:  2004-07       Impact factor: 4.790

2.  Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet.

Authors:  Khalid M Hosny; Mohamed A Kassem; Mohamed M Fouad
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

3.  Detection of atypical texture features in early malignant melanoma.

Authors:  Bijaya Shrestha; Joseph Bishop; Keong Kam; Xiaohe Chen; Randy H Moss; William V Stoecker; Scott Umbaugh; R Joe Stanley; M Emre Celebi; Ashfaq A Marghoob; Giuseppe Argenziano; H Peter Soyer
Journal:  Skin Res Technol       Date:  2010-02       Impact factor: 2.365

4.  Texture analysis of protein distribution images to find differences due to aging and superfusion.

Authors:  S Dutta; B J Barber; S Parameswaran
Journal:  Ann Biomed Eng       Date:  1995 Nov-Dec       Impact factor: 3.934

5.  The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy.

Authors:  Orkun Sarioglu; Fatma Ceren Sarioglu; Ahmet Ergin Capar; Demet Funda Bas Sokmez; Pelin Topkaya; Umit Belet
Journal:  Eur Radiol       Date:  2021-02-09       Impact factor: 5.315

6.  Detection of solid pigment in dermatoscopy images using texture analysis.

Authors:  Anantha Murali; William V. Stoecker; Randy H. Moss
Journal:  Skin Res Technol       Date:  2000-11       Impact factor: 2.365

7.  Clot-based radiomics features predict first pass effect in acute ischemic stroke.

Authors:  Orkun Sarioglu; Fatma C Sarioglu; Ahmet E Capar; Demet Fb Sokmez; Berna D Mete; Umit Belet
Journal:  Interv Neuroradiol       Date:  2021-05-18       Impact factor: 1.764

8.  Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer.

Authors:  Ryder M Schmidt; Rodrigo Delgadillo; John C Ford; Kyle R Padgett; Matthew Studenski; Matthew C Abramowitz; Benjamin Spieler; Yihang Xu; Fei Yang; Nesrin Dogan
Journal:  Sci Rep       Date:  2021-11-23       Impact factor: 4.379

9.  Calibration and segmentation of skin areas in hyperspectral imaging for the needs of dermatology.

Authors:  Robert Koprowski; Sławomir Wilczyński; Zygmunt Wróbel; Barbara Błońska-Fajfrowska
Journal:  Biomed Eng Online       Date:  2014-08-08       Impact factor: 2.819

10.  Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

Authors:  Joanna Jaworek-Korjakowska; Paweł Kłeczek
Journal:  Biomed Res Int       Date:  2016-01-17       Impact factor: 3.411

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