Hierarchical Planar Correlation Clustering for Cell Segmentation

We introduce a novel algorithm for hierarchical clustering on planar graphs we call “Hierarchical Greedy Planar Correlation Clustering” (HGPCC). We formulate hierarchical image segmentation as an ultrametric rounding problem on a superpixel graph where there are edges between superpixels that are adjacent in the image. We apply coordinate descent optimization where updates are based on planar correlation clustering. Planar correlation clustering is NP hard but the efficient PlanarCC solver allows for efficient and accurate approximate inference. We demonstrate HGPCC on problems in segmenting images of cells.

[1]  Julian Yarkony,et al.  Fast Planar Correlation Clustering for Image Segmentation , 2012, ECCV.

[2]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  Fred A. Hamprecht,et al.  Yeast cell detection and segmentation in bright field microscopy , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[4]  Shai Bagon,et al.  Large Scale Correlation Clustering Optimization , 2011, ArXiv.

[5]  Ullrich Köthe,et al.  Globally Optimal Closed-Surface Segmentation for Connectomics , 2012, ECCV.

[6]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  Hervé Delingette,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 , 2012, Lecture Notes in Computer Science.

[8]  Margrit Betke,et al.  Hierarchical Partial Matching and Segmentation of Interacting Cells , 2012, MICCAI.

[9]  Nikhil Bansal,et al.  Correlation Clustering , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[10]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  B. S. Manjunath,et al.  Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs , 2013, EMMCVPR.

[14]  Takeo Kanade,et al.  Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features , 2013, Medical Image Anal..

[15]  Daniel Rueckert,et al.  Application-Driven MRI: Joint Reconstruction and Segmentation from Undersampled MRI Data , 2014, MICCAI.

[16]  Ullrich Köthe,et al.  Probabilistic image segmentation with closedness constraints , 2011, 2011 International Conference on Computer Vision.

[17]  Rentsen Enkhbat,et al.  Cell Segmentation in Phase Contrast Microscopy by Constrained Optimization , 2015, Int. J. E Health Medical Commun..

[18]  Jan Cornelis,et al.  A novel computer-aided lung nodule detection system for CT images. , 2011, Medical physics.

[19]  Charless C. Fowlkes,et al.  Planarity matters: map inference in planar markov random fields with applications to computer vision , 2012 .

[20]  Andrew Zisserman,et al.  Learning to Detect Cells Using Non-overlapping Extremal Regions , 2012, MICCAI.

[21]  Matthieu Guillaumin,et al.  Segmentation Propagation in ImageNet , 2012, ECCV.

[22]  Julian Yarkony,et al.  Cell Detection and Segmentation Using Correlation Clustering , 2014, MICCAI.

[23]  Nir Ailon,et al.  Fitting tree metrics: Hierarchical clustering and phylogeny , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[24]  Lin Yang,et al.  Robust muscle cell segmentation using region selection with dynamic programming , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[25]  Morteza Zadimoghaddam,et al.  Optimal Coalition Structures in Graph Games , 2011, ArXiv.

[26]  Sebastian Nowozin,et al.  Higher-Order Correlation Clustering for Image Segmentation , 2011, NIPS.

[27]  Takeo Kanade,et al.  Interactive cell segmentation based on correction propagation , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).