A lognormal multiplicative cascade model for multifractal characterization of skin ultrasound images

M. Djeddi*, Z. Ouksili, H. Batatia

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biological tissue characterization requires adequate models of the data. Research in ultrasound image modeling has mainly focused on statistical methods. Some authors have addressed the fractal properties of such images using fractional Brownian motion model. However, recent studies have shown that skin ultrasound signals have multifractal properties. This paper proposes a lognormal multiplicative cascade model to explain this multi-fractality. Experimental results on real images show the validity of the model supported by excellent KS tests. The parameters of the model are proposed as indicators to characterize underlying tissues. Successful application to distinguish melanoma from healthy dermis is presented.

Original languageEnglish
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)
PublisherIEEE
Pages1583-1586
Number of pages4
ISBN (Electronic)9781457718588
ISBN (Print)9781457718571
DOIs
Publication statusPublished - 12 Jul 2012
Event9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2012 - Barcelona, Spain
Duration: 2 May 20125 May 2012

Publication series

NameIEEE International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2012
Abbreviated titleISBI 2012
Country/TerritorySpain
CityBarcelona
Period2/05/125/05/12

Keywords

  • lognormal distribution
  • multifractal analysis
  • Multiplicative cascade
  • skin characterization
  • ultrasound images

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'A lognormal multiplicative cascade model for multifractal characterization of skin ultrasound images'. Together they form a unique fingerprint.

Cite this