Texture-based image segmentation using neutrosophic clustering

This study presents an effective segmentation method which is based on neutrosophic clustering with the integration of texture features for images. The proposed method transforms the image into the neutrosophic domain and then extracts the texture features using analogies of human preattentive texture discrimination mechanisms. Finally, the neutrosophic clustering is employed to segment the images. This method can handle the indeterminacy of pixels to have strong clusters and to perform segmentation effectively with the noisy images. Experiments are performed with various types of natural and medical images to exhibit the performance of proposed segmentation method. The evaluation of proposed method has been done with other segmentation methods to measure its performance which shows its robustness for noisy and textured images.

[1]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

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

[3]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[4]  Lotfi A. Zadeh,et al.  Fuzzy logic - a personal perspective , 2015, Fuzzy Sets Syst..

[5]  Florentin Smarandache.  Neutrosophic Logic - A Generalization of the Intuitionistic Fuzzy Logic , 2003, EUSFLAT Conf..

[6]  De-Shuang Huang,et al.  A novel texture image segmentation model based on multi-scale structure , 2014, 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI).

[7]  Yanhui Guo,et al.  A novel image segmentation algorithm based on neutrosophic similarity clustering , 2014, Appl. Soft Comput..

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

[9]  Paul F. Whelan,et al.  Image segmentation based on the integration of colour-texture descriptors - A review , 2011, Pattern Recognit..

[10]  B. S. Manjunath,et al.  Content-based search of video using color, texture, and motion , 1997, Proceedings of International Conference on Image Processing.

[11]  Yanhui Guo,et al.  A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering , 2017, Neural Computing and Applications.

[12]  Jun Ye,et al.  A novel image thresholding algorithm based on neutrosophic similarity score , 2014 .

[13]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[14]  Swati Goel,et al.  A framework for improving misclassification rate of texture segmentation using ICA and Ant Tree clustering algorithm , 2015, International Conference on Computing, Communication & Automation.

[15]  Nikos Dimitropoulos,et al.  Thyroid Texture Representation via Noise Resistant Image Features , 2008, 2008 21st IEEE International Symposium on Computer-Based Medical Systems.

[16]  Reyer Zwiggelaar,et al.  Texture Based Segmentation , 2006, Digital Mammography / IWDM.

[17]  Savita Gupta,et al.  Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set , 2016, Appl. Soft Comput..

[18]  Yanhui Guo,et al.  A Novel Image Segmentation Algorithm Based on Fuzzy C-means Algorithm and Neutrosophic Set , 2008 .

[19]  Ch. Satyanarayana,et al.  Studies on Texture Segmentation Using D- Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering , 2016 .

[20]  Dong ping Tian,et al.  A Review on Image Feature Extraction and Representation Techniques , 2013 .

[21]  R. Bhavani,et al.  Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features , 2016 .

[22]  W D Richard,et al.  Automated texture-based segmentation of ultrasound images of the prostate. , 1996, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[23]  Yuan Zhang,et al.  A Novel Region Merge Algorithm based on Neutrosophic Logic , 2011 .

[24]  Jeong-Mook Lim,et al.  Estimating the Number of Clusters with Database for Texture Segmentation Using Gabor Filter , 2015, ICVS.

[25]  H.-H. Nagel,et al.  Texture-based segmentation of road images , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[26]  Reyer Zwiggelaar,et al.  Texture segmentation using different orientations of GLCM features , 2013, MIRAGE '13.

[27]  Nooshin Nabizadeh,et al.  Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm , 2017, Expert Syst. Appl..

[28]  Reza Azmi,et al.  A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR images , 2011, Journal of medical signals and sensors.

[29]  Ming Zhang,et al.  A neutrosophic approach to image segmentation based on watershed method , 2010, Signal Process..