Neutro-Connectedness Theory, Algorithms and Applications

..................................................................................................................... iii PUBLIC ABSTRACT ........................................................................................................v ACKNOWLEDGMENTS ................................................................................................ vi LIST OF TABLES ............................................................................................................ ix LIST OF FIGURES ............................................................................................................x CHAPTER

[1]  R. Chang,et al.  Tumor detection in automated breast ultrasound images using quantitative tissue clustering. , 2014, Medical physics.

[2]  Xianglong Tang,et al.  Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images , 2010, Pattern Recognit..

[3]  Fei Xu,et al.  An algorithm based on LBPV and MIL for left atrial thrombi detection using transesophageal echocardiography , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[4]  Sheng-Fang Huang,et al.  Neural network analysis applied to tumor segmentation on 3D breast ultrasound images , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[5]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[6]  Peng Jiang,et al.  Learning-based automatic breast tumor detection and segmentation in ultrasound images , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[7]  Alexandre X. Falcão,et al.  Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking , 2012, IEEE Transactions on Image Processing.

[8]  Yan Qiu Chen,et al.  Fuzzy aggregated connectedness for image segmentation , 2001, Pattern Recognit..

[9]  Thomas J. Palmeri,et al.  Encyclopedia of Cognitive Science , 2003 .

[10]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[11]  Youjie Zhou,et al.  Loosecut: Interactive image segmentation with loosely bounded boxes , 2015, 2017 IEEE International Conference on Image Processing (ICIP).

[12]  Fei Xu,et al.  Unsupervised saliency estimation based on robust hypotheses , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[13]  Qiang Wang,et al.  Multiscale superpixel classification for tumor segmentation in breast ultrasound images , 2012, 2012 19th IEEE International Conference on Image Processing.

[14]  Ismail Ben Ayed,et al.  Pseudo-bound Optimization for Binary Energies , 2014, ECCV.

[15]  Leo Grady,et al.  A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[16]  Roberto Casati.  Topology and Cognition , 2006 .

[17]  Woo Kyung Moon,et al.  Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. , 2003, Ultrasound in medicine & biology.

[18]  Jayaram K. Udupa,et al.  Joint graph cut and relative fuzzy connectedness image segmentation algorithm , 2013, Medical Image Anal..

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

[20]  Xianglong Tang,et al.  Multiple-domain knowledge based MRF model for tumor segmentation in breast ultrasound images , 2012, 2012 19th IEEE International Conference on Image Processing.

[21]  Lin Yang,et al.  Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation , 2016, MICCAI.

[22]  Liang Gao,et al.  Phase- and GVF-Based Level Set Segmentation of Ultrasonic Breast Tumors , 2012, J. Appl. Math..

[23]  E. Conant,et al.  A Review of Breast Ultrasound , 2006, Journal of Mammary Gland Biology and Neoplasia.

[24]  Xianglong Tang,et al.  Probability density difference-based active contour for ultrasound image segmentation , 2010, Pattern Recognit..

[25]  Wenbing Tao,et al.  Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor , 2009, IEEE Transactions on Image Processing.

[26]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[27]  Heng-Da Cheng,et al.  Segmentation of ultrasound breast images based on a neutrosophic method , 2010 .

[28]  Dar-Ren Chen,et al.  Automatic Contouring for Breast Tumors in 2-D Sonography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[29]  Mohammad I. Daoud,et al.  Accurate Segmentation of Breast Tumors in Ultrasound Images Using a Custom-Made Active Contour Model and Signal-to-Noise Ratio Variations , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[30]  Ruey-Feng Chang,et al.  Multi-Dimensional Tumor Detection in Automated Whole Breast Ultrasound Using Topographic Watershed , 2014, IEEE Transactions on Medical Imaging.

[31]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  James H. Garrett,et al.  Engineering applications of neural networks , 1993, J. Intell. Manuf..

[33]  Paul Scheunders,et al.  A multivalued image wavelet representation based on multiscale fundamental forms , 2002, IEEE Trans. Image Process..

[34]  W. Gómez,et al.  Active Contours without Edges Applied to Breast Lesions on Ultrasound , 2010 .

[35]  Min Xian,et al.  Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains , 2015, Pattern Recognit..

[36]  L Chen,et al.  Topological structure in visual perception. , 1982, Science.

[37]  Kevin J. Parker,et al.  Multiple Resolution Bayesian Segmentation of Ultrasound Images , 1994, Other Conferences.

[38]  Nam Chul Kim,et al.  RD-Based Seeded Region Growing for Extraction of Breast Tumor in an Ultrasound Volume , 2005, CIS.

[39]  Fei Xu,et al.  EISeg: Effective interactive segmentation , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[40]  Alexandre Xavier Falcao,et al.  Hybrid Approaches for Interactive Image Segmentation Using the Live Markers Paradigm , 2014, IEEE Transactions on Image Processing.

[41]  Lena Gorelick,et al.  GrabCut in One Cut , 2013, 2013 IEEE International Conference on Computer Vision.

[42]  Jorge Stolfi,et al.  The image foresting transform: theory, algorithms, and applications , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  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.

[44]  Zhuowen Tu,et al.  MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[46]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[47]  Min Xian,et al.  A Fully Automatic Breast Ultrasound Image Segmentation Approach Based on Neutro-Connectedness , 2014, 2014 22nd International Conference on Pattern Recognition.

[48]  Fang-Cheng Yeh,et al.  Cell-competition algorithm: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images. , 2005, Ultrasound in medicine & biology.

[49]  Heng-Da Cheng,et al.  Fuzzy subfiber and its application to seismic lithology classification , 1994 .

[50]  Vladimir Kolmogorov,et al.  Graph cut based image segmentation with connectivity priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[51]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[52]  Fei Xu,et al.  A saliency model for automated tumor detection in breast ultrasound images , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[53]  Xianglong Tang,et al.  WENN for individualized cleaning in imbalanced data , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[54]  Yuxuan Wang,et al.  Completely automated segmentation approach for breast ultrasound images using multiple-domain features. , 2012, Ultrasound in medicine & biology.

[55]  Jayaram K. Udupa,et al.  User-Steered Image Segmentation Paradigms: Live Wire and Live Lane , 1998, Graph. Model. Image Process..

[56]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[57]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[58]  Ruey-Feng Chang,et al.  Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound , 2014, Ultrasonic imaging.

[59]  Moi Hoon Yap,et al.  A novel algorithm for initial lesion detection in ultrasound breast images , 2008, Journal of applied clinical medical physics.

[60]  Tore Opsahl,et al.  Clustering in weighted networks , 2009, Soc. Networks.

[61]  Yan Xu,et al.  A modified spatial fuzzy clustering method based on texture analysis for ultrasound image segmentation , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[62]  Michael Brady,et al.  Segmentation of ultrasound B-mode images with intensity inhomogeneity correction , 2002, IEEE Transactions on Medical Imaging.

[63]  Robert Marti,et al.  Simultaneous Lesion Segmentation and Bias Correction in Breast Ultrasound Images , 2011, IbPRIA.

[64]  David P. Doane,et al.  Measuring Skewness: A Forgotten Statistic? , 2011 .

[65]  Kristen Grauman,et al.  Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation , 2013, 2013 IEEE International Conference on Computer Vision.

[66]  Shan Gai,et al.  Reduced quaternion matrix-based sparse representation and its application to colour image processing , 2019, IET Image Process..

[67]  Hamid R. Tizhoosh,et al.  Segmentation of Breast Ultrasound Images Using Neural Networks , 2011, EANN/AIAI.

[68]  Feiping Nie,et al.  Interactive Natural Image Segmentation via Spline Regression , 2009, IEEE Transactions on Image Processing.

[69]  Shuiwang Ji,et al.  Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.

[70]  H. D. Cheng,et al.  A novel segmentation method for breast ultrasound images based on neutrosophic l-means clustering. , 2012, Medical physics.

[71]  Daniel Cremers,et al.  Tree Shape Priors with Connectivity Constraints Using Convex Relaxation on General Graphs , 2013, ICCV.

[72]  Chung-Ming Chen,et al.  Cell-based graph cut for segmentation of 2D/3D sonographic breast images , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[73]  Jayaram K. Udupa,et al.  An ultra-fast user-steered image segmentation paradigm: live wire on the fly , 2000, IEEE Transactions on Medical Imaging.

[74]  Fei Xu,et al.  Automatic Breast Ultrasound Image Segmentation: A Survey , 2017, Pattern Recognit..

[75]  Heng-Da Cheng,et al.  A novel automatic seed point selection algorithm for breast ultrasound images , 2008, 2008 19th International Conference on Pattern Recognition.

[76]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[77]  Dimitris N. Metaxas,et al.  Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions , 2003, IEEE Transactions on Medical Imaging.

[78]  J. Udupa,et al.  Iterative relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[79]  Fei Xu,et al.  Neutro-Connectedness Cut , 2015, IEEE Transactions on Image Processing.

[80]  A. Jemal,et al.  Cancer statistics, 2015 , 2015, CA: a cancer journal for clinicians.

[81]  D. Boukerroui,et al.  Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. , 1998, European journal of ultrasound : official journal of the European Federation of Societies for Ultrasound in Medicine and Biology.

[82]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[83]  P Kovesi,et al.  Phase congruency: A low-level image invariant , 2000, Psychological research.

[84]  Dar-Ren Chen,et al.  Watershed segmentation for breast tumor in 2-D sonography. , 2004, Ultrasound in medicine & biology.

[85]  Zhuowen Tu,et al.  Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[86]  Ying Wang,et al.  A Benchmark for Breast Ultrasound Image Segmentation (BUSIS) , 2018, ArXiv.

[87]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[88]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[89]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[90]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[91]  Heng-Da Cheng,et al.  Local-weighted Citation-kNN algorithm for breast ultrasound image classification , 2015 .

[92]  Azriel Rosenfeld,et al.  Fuzzy Digital Topology , 1979, Inf. Control..

[93]  Douglas L. Jones,et al.  Detection of lines and boundaries in speckle images-application to medical ultrasound , 1999, IEEE Transactions on Medical Imaging.

[94]  Yuxuan Wang,et al.  Robust multiple cue fusion-based high-speed and nonrigid object tracking algorithm for short track speed skating , 2016, J. Electronic Imaging.

[95]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[96]  Toby Sharp,et al.  Image segmentation with a bounding box prior , 2009, 2009 IEEE 12th International Conference on Computer Vision.