Three techniques for automatic extraction of corpus callosum in structural midsagittal brain MR images: Valley Matching, Evolutionary Corpus Callosum Detection and Hybrid method

Dagdeviren Z. A., Oguz K., ERDEM M. G.

Engineering Applications of Artificial Intelligence, vol.31, pp.101-115, 2014 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 31
  • Publication Date: 2014
  • Doi Number: 10.1016/j.engappai.2013.10.004
  • Journal Name: Engineering Applications of Artificial Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.101-115
  • Keywords: Brain MR image, Corpus callosum, Crowding, Genetic algorithm, Histogram processing, Medical image segmentation
  • Manisa Celal Bayar University Affiliated: No

Abstract

Corpus callosum (CC) is an important structure for medical image registration. We propose three novel fully automated for the extraction of CC. Our first algorithm, Valley matching (VM), is based on fixed searched range in histogram processing and uses prior anatomical information for locating CC. The second one, Evolutionary CC Detection (ECD), based on genetic algorithm presents a new fitness function that uses anatomical ratios, instead of fixed prior knowledge without the need for preprocessing. The final one, called Evolutionary Valley Matching (EVM), takes advantages of the strong points of the first and second algorithms. The search space defined for ECD is reduced by VM which uses crowding method to find the peaks in the multi-modal histogram. Another important contribution of this study is that there is no existing method using genetic algorithm for extracting CC. Our proposed algorithms perform with the success rates up to 95.5%. © 2013 Elsevier Ltd.