An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering

Abstract A reliable nuclei segmentation is still an open-ended problem, especially in the breast cancer histology images. For the same, this paper proposes an intelligent gravitational search algorithm based superpixel clustering method for automatic nuclei segmentation. In the proposed method, a novel variant of gravitational search algorithm, intelligent gravitational search algorithm, is employed to obtain the optimal cluster centroids. The experimental and statistical results evince that the proposed variant surpasses existing meta-heuristic algorithms on 47 benchmark functions belonging to different problem categories i.e., unimodal, multimodal, and real-parameter single objective optimization problems of CEC, 2013. Further, the segmentation accuracy of the proposed method is examined on H&E stained estrogen receptor positive (ER+) breast cancer images. Experiments affirm that the proposed method is comparatively an efficacious and accurate method for segmenting the nuclei within breast cancer histology images.

[1]  Greg Mori,et al.  Guiding model search using segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[2]  Antony Galton,et al.  Unsupervised Superpixel-Based Segmentation of Histopathological Images with Consensus Clustering , 2017, MIUA.

[3]  Himanshu Mittal,et al.  Classification of Histopathological Images Through Bag-of-Visual-Words and Gravitational Search Algorithm , 2018, SocProS.

[4]  Alexis B. Carter,et al.  Computational Pathology: A Path Ahead. , 2016, Archives of pathology & laboratory medicine.

[5]  Koray Kayabol,et al.  Mixture-Based Superpixel Segmentation and Classification of SAR Images , 2016, IEEE Geoscience and Remote Sensing Letters.

[6]  Wei-Chang Yeh,et al.  Accelerated Simplified Swarm Optimization with Exploitation Search Scheme for Data Clustering , 2015, PloS one.

[7]  Max A. Viergever,et al.  Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[8]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[9]  Gadadhar Sahoo,et al.  A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification , 2014 .

[10]  Bernhard C. Geiger,et al.  Semi-supervised cross-entropy clustering with information bottleneck constraint , 2017, Inf. Sci..

[11]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[12]  Mitko Veta,et al.  Going fully digital: Perspective of a Dutch academic pathology lab , 2013, Journal of pathology informatics.

[13]  Yongming Li,et al.  Automatic cell nuclei segmentation and classification of breast cancer histopathology images , 2016, Signal Process..

[14]  Pangao Kou,et al.  Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system , 2014, Neurocomputing.

[15]  Saman Sinaie,et al.  SOLVING SHORTEST PATH PROBLEM USING GRAVITATIONAL SEARCH ALGORITHM AND NEURAL NETWORKS , 2010 .

[16]  Richard S. Zemel,et al.  Learning and Incorporating Top-Down Cues in Image Segmentation , 2006, ECCV.

[17]  Stefano Soatto,et al.  Class segmentation and object localization with superpixel neighborhoods , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[18]  Stefan Preitl,et al.  Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems , 2012, IEEE Transactions on Industrial Informatics.

[19]  Jian Yang,et al.  Superpixel-based segmentation for multi-temporal PolSAR images , 2017, 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL).

[20]  Xiangtao Li,et al.  A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering , 2011, Expert Syst. Appl..

[21]  Hossein Nezamabadi-pour,et al.  Disruption: A new operator in gravitational search algorithm , 2011, Sci. Iran..

[22]  Yan Wang,et al.  Gravitational search algorithm combined with chaos for unconstrained numerical optimization , 2014, Appl. Math. Comput..

[23]  Mohsen Davarynejad,et al.  Mass-Dispersed Gravitational Search Algorithm for Gene Regulatory Network Model Parameter Identification , 2012, SEAL.

[24]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[25]  Andrew Janowczyk,et al.  Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases , 2016, Journal of pathology informatics.

[26]  Max A. Viergever,et al.  Breast Cancer Histopathology Image Analysis: A Review , 2014, IEEE Transactions on Biomedical Engineering.

[27]  Xin-She Yang,et al.  A literature survey of benchmark functions for global optimisation problems , 2013, Int. J. Math. Model. Numer. Optimisation.

[28]  Ujjwal Maulik,et al.  Genetic algorithm-based clustering technique , 2000, Pattern Recognit..

[29]  Taher Niknam,et al.  Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm , 2012 .

[30]  Harish Sharma,et al.  Leukocyte segmentation in tissue images using differential evolution algorithm , 2013, Swarm Evol. Comput..

[31]  Lin Yang,et al.  Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review , 2016, IEEE Reviews in Biomedical Engineering.

[32]  A. Srinivasa Reddy,et al.  Shuffled Differential Evolution-Based Combined Heat and Power Economic Dispatch , 2018, Soft Computing in Data Analytics.

[33]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[34]  Meena Mahajan,et al.  The planar k-means problem is NP-hard , 2012, Theor. Comput. Sci..

[35]  Andrew Lewis,et al.  Adaptive gbest-guided gravitational search algorithm , 2014, Neural Computing and Applications.

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

[37]  Himanshu Mittal,et al.  An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm , 2018, Eng. Appl. Artif. Intell..

[38]  Ahmad Hakimi,et al.  Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier , 2015, Appl. Math. Comput..

[39]  A. Huisman,et al.  Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images , 2013, PloS one.

[40]  Sakti Prasad Ghoshal,et al.  A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems , 2012 .

[41]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[42]  Janez Brest,et al.  Real Parameter Single Objective Optimization using self-adaptive differential evolution algorithm with more strategies , 2013, 2013 IEEE Congress on Evolutionary Computation.

[43]  A. Chatterjee,et al.  A maiden application of gravitational search algorithm with wavelet mutation for the solution of economic load dispatch problems , 2012, Int. J. Bio Inspired Comput..

[44]  Raju Pal,et al.  Chaotic Kbest gravitational search algorithm (CKGSA) , 2016, 2016 Ninth International Conference on Contemporary Computing (IC3).

[45]  Ahmed El-Shafie,et al.  A modified gravitational search algorithm for slope stability analysis , 2012, Eng. Appl. Artif. Intell..

[46]  J P Vink,et al.  Efficient nucleus detector in histopathology images , 2013, Journal of microscopy.

[47]  Mohamed F. Tolba,et al.  On Combining Nature-Inspired Algorithms for Data Clustering , 2017 .

[48]  Amir Masoud Rahmani,et al.  Automatic data clustering using continuous action-set learning automata and its application in segmentation of images , 2017, Appl. Soft Comput..

[49]  Paria Mehrani,et al.  Superpixels and Supervoxels in an Energy Optimization Framework , 2010, ECCV.

[50]  Mahdi Aliyari Shoorehdeli,et al.  Stability analysis of particle dynamics in gravitational search optimization algorithm , 2016, Inf. Sci..

[51]  Alexei A. Efros,et al.  Automatic photo pop-up , 2005, SIGGRAPH 2005.

[52]  Junzhou Huang,et al.  Automatic extraction of cell nuclei from H&E-stained histopathological images , 2017, Journal of medical imaging.

[53]  F. Mardukhi,et al.  An approach for web services composition based on QoS and gravitational search algorithm , 2009, 2009 International Conference on Innovations in Information Technology (IIT).

[54]  J. Ferlay,et al.  Global estimates of cancer prevalence for 27 sites in the adult population in 2008 , 2013, International journal of cancer.

[55]  Seyed-Hamid Zahiri,et al.  Decision function estimation using intelligent gravitational search algorithm , 2012, Int. J. Mach. Learn. Cybern..

[56]  Li Pei,et al.  Path planning of unmanned aerial vehicle based on improved gravitational search algorithm , 2012 .

[57]  H Llewellyn,et al.  Observer variation, dysplasia grading, and HPV typing: a review. , 2000, American journal of clinical pathology.

[58]  Jianzhong Zhou,et al.  Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm , 2011 .

[59]  Zhiguo Jiang,et al.  Histopathological Whole Slide Image Analysis Using Context-Based CBIR , 2018, IEEE Transactions on Medical Imaging.

[60]  Jian Sun,et al.  Lazy snapping , 2004, SIGGRAPH 2004.

[61]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[62]  Himanshu Mittal,et al.  An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering , 2018, Evolutionary Intelligence.

[63]  Humberto Bustince,et al.  A gravitational approach to edge detection based on triangular norms , 2010, Pattern Recognit..

[64]  Chanho Jung,et al.  Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification , 2010, IEEE Transactions on Biomedical Engineering.

[65]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[66]  Himanshu Mittal,et al.  cKGSA Based Fuzzy Clustering Method for Image Segmentation of RGB-D Images , 2018, 2018 Eleventh International Conference on Contemporary Computing (IC3).

[67]  Hossein Nezamabadi-pour,et al.  GGSA: A Grouping Gravitational Search Algorithm for data clustering , 2014, Eng. Appl. Artif. Intell..

[68]  Yu Zhang,et al.  Immunity-Based Gravitational Search Algorithm , 2012, ICICA.

[69]  Arpan Kumar Kar,et al.  Swarm Intelligence: A Review of Algorithms , 2017 .

[70]  Lasse Riis Østergaard,et al.  Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides , 2017, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[71]  Jacek Tabor,et al.  Semi-supervised model-based clustering with controlled clusters leakage , 2017, Expert Syst. Appl..

[72]  Mingru Zhao,et al.  A Data Clustering Algorithm Using Cuckoo Search , 2016 .

[73]  Sven J. Dickinson,et al.  Multiscale Symmetric Part Detection and Grouping , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[74]  Hao Liu,et al.  A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems , 2019, J. Intell. Manuf..

[75]  Hang Yu,et al.  Self-Adaptive Gravitational Search Algorithm With a Modified Chaotic Local Search , 2017, IEEE Access.

[76]  Daniel Cohen-Or,et al.  Constraints as Features , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[77]  Dan Simon,et al.  Evolutionary Optimization Algorithms , 2013 .

[78]  Xiaoming Chang,et al.  A chaotic digital secure communication based on a modified gravitational search algorithm filter , 2012, Inf. Sci..

[79]  Long Quan,et al.  A novel data clustering algorithm based on modified gravitational search algorithm , 2017, Eng. Appl. Artif. Intell..

[80]  Mohammad Bagher Dowlatshahi,et al.  Using Gravitational Search Algorithm for Finding Near-optimal Base Station Location in Two-Tiered WSNs , 2012 .

[81]  Raju Pal,et al.  Data clustering using enhanced biogeography-based optimization , 2017, 2017 Tenth International Conference on Contemporary Computing (IC3).

[82]  Xin-She Yang,et al.  Nature-Inspired Optimization Algorithms: Challenges and Open Problems , 2020, J. Comput. Sci..

[83]  Han Xiao,et al.  Parameters identification of chaotic system by chaotic gravitational search algorithm , 2012, Chaos, Solitons & Fractals.

[84]  Hossein Nezamabadi-pour,et al.  A quantum inspired gravitational search algorithm for numerical function optimization , 2014, Inf. Sci..