Medical image classification using spatial adjacent histogram based on adaptive local binary patterns

Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches.

[1]  Timothy F. Cootes,et al.  Statistical models of appearance for medical image analysis and computer vision , 2001, SPIE Medical Imaging.

[2]  Jelena Kovacevic,et al.  A multiresolution approach to automated classification of protein subcellular location images , 2007, BMC Bioinformatics.

[3]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.

[4]  Matti Pietikäinen,et al.  Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features , 2009, SCIA.

[5]  Alberto Del Bimbo,et al.  The Mesh-LBP: A Framework for Extracting Local Binary Patterns From Discrete Manifolds , 2015, IEEE Transactions on Image Processing.

[6]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[7]  Hossein Pourghassem,et al.  Content-based medical image classification using a new hierarchical merging scheme , 2008, Comput. Medical Imaging Graph..

[8]  Georgios Dounias,et al.  Pap-smear Benchmark Data For Pattern Classification , 2005 .

[9]  Mario Vento,et al.  Benchmarking HEp-2 Cells Classification Methods , 2013, IEEE Transactions on Medical Imaging.

[10]  Zhengzhi Wang,et al.  Building global image features for scene recognition , 2012, Pattern Recognit..

[11]  Cheng Wang,et al.  A novel extended local-binary-pattern operator for texture analysis , 2008, Inf. Sci..

[12]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[13]  Liming Chen,et al.  Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  D. Altman,et al.  Multiple significance tests: the Bonferroni method , 1995, BMJ.

[15]  Guizhong Liu,et al.  Scale- and Rotation-Invariant Local Binary Pattern Using Scale-Adaptive Texton and Subuniform-Based Circular Shift , 2012, IEEE Transactions on Image Processing.

[16]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[17]  Andreas Uhl,et al.  A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification , 2015, Pattern Recognit..

[18]  Xudong Jiang,et al.  Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism , 2013, IEEE Transactions on Image Processing.

[19]  Kazuhiro Fukui,et al.  Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns , 2011, PSIVT.

[20]  Loris Nanni,et al.  Survey on LBP based texture descriptors for image classification , 2012, Expert Syst. Appl..

[21]  Shu Liao,et al.  Face Recognition by Using Elongated Local Binary Patterns with Average Maximum Distance Gradient Magnitude , 2007, ACCV.

[22]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[23]  Zhenhua Guo,et al.  Hierarchical multiscale LBP for face and palmprint recognition , 2010, 2010 IEEE International Conference on Image Processing.

[24]  N. Senthilkumaran,et al.  Image Segmentation - A Survey of Soft Computing Approaches , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[25]  Pierre Jannin,et al.  Automatic Phases Recognition in Pituitary Surgeries by Microscope Images Classification , 2010, IPCAI.

[26]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[27]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Mudar Sarem,et al.  Robust image region descriptor using local derivative ordinal binary pattern , 2015, J. Electronic Imaging.

[29]  Andreas Uhl,et al.  Color treatment in endoscopic image classification using multi-scale local color vector patterns , 2012, Medical Image Anal..

[30]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Hyeran Byun,et al.  Applications of Support Vector Machines for Pattern Recognition: A Survey , 2002, SVM.

[32]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[33]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.