Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Skin Melanoma Detection in Microscopic Images Using Hmm-Based Asymmetric Analysis and Expectation Maximization Publisher Pubmed



Rastghalam R1 ; Danyali H1 ; Helfroush MS1 ; Celebi ME2 ; Mokhtari M3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Elcetrical Engineering, Shiraz University of Technology, Fars, Shiraz, Iran
  2. 2. Department of Computer Science, University of Central Arkansas, Conway, AR, United States
  3. 3. Isfahan University of Medical Sciences, Isfahan, Iran

Source: IEEE Journal of Biomedical and Health Informatics Published:2021


Abstract

Melanoma is one of the deadliest types of skin cancer with increasing incidence. The most definitive diagnosis method is the histopathological examination of the tissue sample. In this paper, a melanoma detection algorithm is proposed based on decision-level fusion and a Hidden Markov Model (HMM), whose parameters are optimized using Expectation Maximization (EM) and asymmetric analysis. The texture heterogeneity of the samples is determined using asymmetric analysis. A fusion-based HMM classifier trained using EM is introduced. For this purpose, a novel texture feature is extracted based on two local binary patterns, namely local difference pattern (LDP) and statistical histogram features of the microscopic image. Extensive experiments demonstrate that the proposed melanoma detection algorithm yields a total error of less than 0.04%. © 2013 IEEE.
Related Docs
1. Computer Aided Measurement of Melanoma Depth of Invasion in Microscopic Images, Micron (2014)
2. Nonlinear Analysis of the Contour Boundary Irregularity of Skin Lesion Using Lyapunov Exponent and K-S Entropy, Journal of Medical and Biological Engineering (2017)
3. Automatic Detection of Acute Lymphoblastic Leukaemia Based on Extending the Multifractal Features, IET Image Processing (2020)
4. Differentiation Between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment Using Multi-Scale Texture Analysis of Ct Images, Journal of Biomedical Physics and Engineering (2022)
5. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data, Iranian Journal of Radiology (2015)
6. Investigating the Feasibility of Differentiating Ms Active Lesions From Inactive Ones Using Texture Analysis and Machine Learning Methods in Dwi Images, Multiple Sclerosis and Related Disorders (2024)
7. Segmenting Skin Biopsy Images With Coarse and Sparse Annotations Using U-Net, Journal of Digital Imaging (2022)
8. Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation, Diagnostics (2022)
Experts (# of related papers)
View all Related Experts
Alireza Mehri-Dehnavi (1)
Raheleh Kafieh (1)