Unsupervised Multimodal Magnetic Resonance Images Segmentation and Multiple Sclerosis Lesions Extraction based on Edge and Texture Features

Authors

  • Tannaz AKBARPOUR Sahand University of Technology
  • Mousa SHAMSI Sahand University of Technology
  • Sabalan DANESHVAR Electrical and computer engineering faculty, Tabriz University
  • Masoud POOREISA medicine faculty, Tabriz University of Medical science

Keywords:

Multiple sclerosis, Segmentation, Multichannel Magnetic Resonance Imaging (MRI), Wavelet, Energy, Entropy

Abstract

Segmentation of Multiple Sclerosis (MS) lesions is a crucial part of MS diagnosis and therapy. Segmentation of lesions is usually performed manually, exposing this process to human errors. Thus, exploiting automatic and semi-automatic methods is of interest. In this paper, a new method is proposed to segment MS lesions from multichannel MRI data (T1-W and T2-W). For this purpose, statistical features of spatial domain and wavelet coefficients of frequency domain are extracted for each pixel of skull-stripped images to form a feature vector. An unsupervised clustering algorithm is applied to group pixels and extracts lesions. Experimental results demonstrate that the proposed method is better than other state of art and contemporary methods of segmentation in terms of Dice metric, specificity, false-positive-rate, and Jaccard metric.

Author Biographies

Tannaz AKBARPOUR, Sahand University of Technology

Biomedical Engineering department

Mousa SHAMSI, Sahand University of Technology

Biomedical Engineering department

Sabalan DANESHVAR, Electrical and computer engineering faculty, Tabriz University

biomedical engineering department

Masoud POOREISA, medicine faculty, Tabriz University of Medical science

Radiology De3partment

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Published

29.07.2017

How to Cite

1.
AKBARPOUR T, SHAMSI M, DANESHVAR S, POOREISA M. Unsupervised Multimodal Magnetic Resonance Images Segmentation and Multiple Sclerosis Lesions Extraction based on Edge and Texture Features. Appl Med Inform [Internet]. 2017 Jul. 29 [cited 2024 Jun. 18];39(1-2):30-4. Available from: https://ami.info.umfcluj.ro/index.php/AMI/article/view/619

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