Segmentation of Cells with Partial Occlusion and Part Configuration Constraint Using Evolutionary Computation

We propose a method for targeted segmentation that identifies and delineates only those spatially-recurring objects that conform to specific geometrical, topological and appearance priors. By adopting a "tribes"-based, global genetic algorithm, we show how we incorporate such priors into a faithful objective function unconcerned about its convexity. We evaluated our framework on a variety of histology and microscopy images to segment potentially overlapping cells with complex topology. Our experiments confirmed the generality, reproducibility and improved accuracy of our approach compared to competing methods.

[1]  Manohar Kuse,et al.  A Classification Scheme for Lymphocyte Segmentation in H&E Stained Histology Images , 2010, ICPR Contests.

[2]  Yuri Boykov,et al.  Globally optimal segmentation of multi-region objects , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Stella X. Yu,et al.  Pop out many small structures from a very large microscopic image , 2011, Medical Image Anal..

[4]  Marcin Grzegorzek,et al.  Counting Lymphocytes in Histopathology Images Using Connected Components , 2010, ICPR Contests.

[5]  Timothy F. Cootes,et al.  Combining point distribution models with shape models based on finite element analysis , 1994, Image Vis. Comput..

[6]  Mark Jenkinson,et al.  Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration , 2011, MICCAI.

[7]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[8]  Manohar Kuse,et al.  Local isotropic phase symmetry measure for detection of beta cells and lymphocytes , 2011, Journal of pathology informatics.

[9]  Peng Zhao,et al.  Supervised learning-based cell image segmentation for P53 immunohistochemistry , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Alasdair Turner,et al.  Obtaining Multiple Distinct Solutions with Genetic Algorithm Niching Methods , 1996, PPSN.

[11]  Shishir K. Shah,et al.  Embedding Topic Discovery in Conditional Random Fields Model for Segmenting Nuclei Using Multispectral Data , 2012, IEEE Transactions on Biomedical Engineering.

[12]  Anant Madabhushi,et al.  Adaptive Energy Selective Active Contour with Shape Priors for Nuclear Segmentation and Gleason Grading of Prostate Cancer , 2011, MICCAI.

[13]  Hans-Paul Schwefel,et al.  Parallel Problem Solving from Nature — PPSN IV , 1996, Lecture Notes in Computer Science.

[14]  Li Cheng,et al.  Discriminative Segmentation of Microscopic Cellular Images , 2011, MICCAI.

[15]  Lisa Tang,et al.  Simulation of Ground-Truth Validation Data Via Physically- and Statistically-Based Warps , 2008, MICCAI.

[16]  Selim Aksoy,et al.  Recognizing Patterns in Signals, Speech, Images and Videos , 2010, Lecture Notes in Computer Science.

[17]  Lin Yang,et al.  Automatic Image Analysis of Histopathology Specimens Using Concave Vertex Graph , 2008, MICCAI.

[18]  Metin Nafi Gürcan,et al.  Pattern Recognition in Histopathological Images: An ICPR 2010 Contest , 2010, ICPR Contests.

[19]  Georgios Tziritas,et al.  Lymphocyte segmentation using the transferable belief model , 2010, ICPR 2010.