Cervical cancer histology image identification method based on texture and lesion area features.

The issue of an automated approach for detecting cervical cancer is proposed to improve the accuracy of recognition. Firstly, the cervical cancer histology source images are needed to use image preprocessing for reducing the impact brought by noise of images as well as the impact on subsequent precise feature extraction brought by irrelevant background. Secondly, the images are grouped into ten vertical images and the information of texture feature is extracted by Grey Level Co-occurrence Matrix (GLCM). GLCM is an effective tool to analyze the features of texture. The textures of different diseases in the source image of Cervical Cancer Histology (such as contrast, correlation, entropy, uniformity and energy, etc.) can all be obtained in this way. Thirdly, the image is segmented by using K-means clustering and Marker-controlled watershed Algorithm. And each vertical image is divided into three layers to calculate the areas of different layers. Based on GLCM and lesion area features, the tissues are investigated with segmentation by using Support Vector Machine (SVM) method. Finally, the results show that it is effective and feasible to recognize cervical cancer by automated approach and verified by experiment.

[1]  Malay Kishore Dutta,et al.  Classification of glaucoma based on texture features using neural networks , 2014, 2014 Seventh International Conference on Contemporary Computing (IC3).

[2]  Elaine Shi,et al.  Private and Continual Release of Statistics , 2010, TSEC.

[3]  Martial Guillaud,et al.  Subvisual chromatin changes in cervical epithelium measured by texture image analysis and correlated with HPV. , 2005, Gynecologic oncology.

[4]  Yi-Ying Wang,et al.  A Color-Based Approach for Automated Segmentation in Tumor Tissue Classification , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Deborah B. Thompson,et al.  An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN) , 2000, The Journal of pathology.

[6]  Alessia Bramanti,et al.  Cluster Analysis boosted watershed segmentation of neurological image , 2011, 2011 4th International Congress on Image and Signal Processing.

[7]  David W. S. Wong,et al.  An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics , 2013 .

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

[9]  Long Bao,et al.  Sequence-to-Sequence Similarity-Based Filter for Image Denoising , 2016, IEEE Sensors Journal.

[10]  Mohamed-Jalal Fadili,et al.  Image Decomposition and Separation Using Sparse Representations: An Overview , 2010, Proceedings of the IEEE.

[11]  Azriel Rosenfeld,et al.  A Medial Axis Transformation for Grayscale Pictures , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Aristidis Likas,et al.  The global kernel k-means clustering algorithm , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[13]  Junior Barrera,et al.  Method to support diagnosis of cervical intraepithelial neoplasia (CIN) based on structural analysis of histological images , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

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

[15]  Shi-Jinn Horng,et al.  Parallel Computation of the Euclidean Distance Transform on a Three-Dimensional Image Array , 2003, IEEE Trans. Parallel Distributed Syst..

[16]  Mrinal Kanti Bhowmik,et al.  Automated Cervical Cancer Detection Using Pap Smear Images , 2014, SocProS.

[17]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[18]  R. Joe Stanley,et al.  Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis With Fusion-Based Classification , 2016, IEEE Journal of Biomedical and Health Informatics.

[19]  Françoise Peyrin,et al.  A new method for analyzing local shape in three-dimensional images based on medial axis transformation , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Aijuan Dong,et al.  Detection of breast tumor candidates using marker-controlled watershed segmentation and morphological analysis , 2012, 2012 IEEE Southwest Symposium on Image Analysis and Interpretation.

[21]  Jacob Goldberger,et al.  Hierarchical Image Segmentation Using Correlation Clustering , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[22]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[23]  D. T. Lee,et al.  Medial Axis Transformation of a Planar Shape , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Adhi Susanto,et al.  Principal Component Analysis combined with First Order Statistical Method for Breast Thermal Images Classification , 2011 .

[25]  Gholamreza Akbarizadeh,et al.  Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images , 2015, IET Comput. Vis..

[26]  Jingfeng Guo,et al.  Image Texture Feature Extraction Method Based on Regional Average Binary Gray Level Difference Co-occurrence Matrix , 2011, 2011 International Conference on Virtual Reality and Visualization.

[27]  R. Joe Stanley,et al.  A fusion-based approach for uterine cervical cancer histology image classification , 2013, Comput. Medical Imaging Graph..

[28]  Junior Barrera,et al.  Structural Analysis of Histological Images to Aid Diagnosis of Cervical Cancer , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[29]  Danny Crookes,et al.  Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN) , 2009, IEEE Journal of Selected Topics in Signal Processing.

[30]  Tiexiang Wen,et al.  Segmenting multiple overlapping Nuclei in H&E Stained Breast Cancer Histopathology Images based on an improved watershed , 2015 .

[31]  Yu-Hsiang Fu,et al.  Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition , 2012, IEEE Transactions on Image Processing.