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Tissue Segmentation Methods Using 2D Histogram Matching in a Sequence of MR Brain Images

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New Approaches in Intelligent Image Analysis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 108))

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Abstract

MR brain image sequences are characterized by a specific structure and intra- and inter-image correlation but most of the existing histogram segmentation methods do not consider them. We address this issue by proposing a method for tissue segmentation using 2D histogram matching (TS-2DHM). Our 2D histogram is produced from a sum of co-occurrence matrices of each MR image. Two types of model 2D histograms are constructed: an intra-tissue 2D histogram for separate tissue regions and an inter-tissue edge 2D histogram. Firstly, we divide a MR image sequence into a few subsequences using wave hedges distance between 2D histograms of consecutive MR images. Then we save and clear out inter-tissue edge entries in each test 2D histogram, match the test 2D histogram segments in a percentile interval and extract the most representative entries for each tissue, which are used for kNN classification after distance learning. We apply the matching using LUT and two ways of distance metric learning: LMNN and NCA. Finally, segmentation of the test MR image is performed using back projection with majority vote between the probability maps of each tissue region, where the inter-tissue edge entries are added with equal weights to the corresponding main tissues. The proposed algorithm has been evaluated with IBSR 18 and 20, and BrainWeb data sets and showed results comparable with state-of-the-art segmentation algorithms, although it does not consider specific shape and ridges of brain tissues. Its benefits are modest execution time, robustness to outliers and adaptation to different 2D histogram distributions.

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Authors and Affiliations

  1. Department of Radio Communications and Video Technologies, Technical University of Sofia, 8 Kl. Ohridski Blvd., 1000, Sofia, Bulgaria

    Vladimir Kanchev & Roumen Kountchev

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  1. Vladimir Kanchev
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  2. Roumen Kountchev
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Correspondence to Vladimir Kanchev .

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  1. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

  2. University of Hyogo, Chuo-ku, Japan

    Kazumi Nakamatsu

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Kanchev, V., Kountchev, R. (2016). Tissue Segmentation Methods Using 2D Histogram Matching in a Sequence of MR Brain Images. In: Kountchev, R., Nakamatsu, K. (eds) New Approaches in Intelligent Image Analysis. Intelligent Systems Reference Library, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-319-32192-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-32192-9_6

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