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A new Probabilistic Active Contour region-based method for multiclass medical image segmentation.

In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours an...

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Published in:Medical & Biological Engineering & Computing Vol. 57; no. 3; pp. 565 - 577
Main Authors: Arce-Santana, Edgar R., Mejia-Rodriguez, Aldo R., Martinez-Peña, Enrique, Alba, Alfonso, Mendez, Martin, Scalco, Elisa, Mastropietro, Alfonso, Rizzo, Giovanna
Format: Journal Article
Published: Springer Nature Mar2019
Online Access:View this record in EBSCOhost
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      dt: Mar2019
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      pub: Springer Nature
      place: New York, New York
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        atl: A new Probabilistic Active Contour region-based method for multiclass medical image segmentation.
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          Arce-Santana, Edgar R.
          Mejia-Rodriguez, Aldo R.
          Martinez-Peña, Enrique
          Alba, Alfonso
          Mendez, Martin
          Scalco, Elisa
          Mastropietro, Alfonso
          Rizzo, Giovanna
        affil: Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
      sug:
        subj:
          Image Processing, Computer Assisted Methods
          Brain
          Magnetic Resonance Imaging Methods
          Probability
          Algorithms
          Cerebrospinal Fluid
          Clinical Assessment Tools
          Scales
      ab: In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed. Graphical Abstract.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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