AUTOMATED BRAIN LESION CLASSIFICATION USING HYBRID FUZZY C-MEANS WITH CORRELATION TEMPLATE AND WAVELET TRANSFORM

Authors

  • Ayuni Fateeha Muda Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Norhashimah Mohd Saad Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Low Yin Fen Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Abdul Rahim Abdullah Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Nazreen Waeleh Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.9449

Keywords:

Segmentation, brain lesion, Fuzzy C-Means (FCM), correlation template, wavelet

Abstract

This paper presents a new technique for automatically detecting and characterizing major brain lesions for diffusion-weighted imaging. The analytical framework consists of pre-processing, segmentation, features extraction and classification. For segmentation process, Fuzzy C-Means integrated with correlation template are proposed to detect the lesion region. The algorithm performance is evaluated using Jaccard and both false positive and false negative rates. Next, the features from wavelet transform are extracted from the region and fed into the rule-based classifier. Results demonstrated that FCM with correlation template offered the best performance for acute stroke segmentation with the highest rate of 0.77 Jaccard index. The classification accuracy for acute stroke, tumor, chronic stroke and necrosis are 94%, 97, 63% and 60%. In conclusion, the proposed hybrid analysis has the potential to be explored as a computer-aided tool to detect and diagnose of human brain lesion.

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Published

2016-07-26

Issue

Vol. 78 No. 7-5: Biomedical Signal, Image and Devices

Section

Science and Engineering

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How to Cite

AUTOMATED BRAIN LESION CLASSIFICATION USING HYBRID FUZZY C-MEANS WITH CORRELATION TEMPLATE AND WAVELET TRANSFORM. (2016). Jurnal Teknologi, 78(7-5). https://doi.org/10.11113/jt.v78.9449