Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization

Texture is one of the most important properties of visual surface that helps in discriminating one object from another or an object from background. The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects its input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. This paper proposes an enhancement extraction method for accurate extracting features for efficient image representation it based on SOM neural network. In this approach, we apply three different partitioning approaches as a region of interested (ROI) selection methods for extracting different accurate textural features from medical image as a primary step of our extraction method. Fisherfaces feature selection is used, for selecting discriminated features form extracted textural features. Experimental result showed the high accuracy of medical image categorization with our proposed extraction method. Experiments held on Mammographic Image Analysis Society (MIAS) dataset.

[1]  Maria Petrou,et al.  Texture anisotropy in 3-D images , 1999, IEEE Trans. Image Process..

[2]  Carlos E. Thomaz,et al.  A multivariate statistical analysis of the developing human brain in preterm infants , 2007, Image Vis. Comput..

[3]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[5]  Bipin C. Desai,et al.  Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion , 2008, Comput. Medical Imaging Graph..

[6]  E Le Rumeur,et al.  MRI texture analysis on texture test objects, normal brain and intracranial tumors. , 2003, Magnetic resonance imaging.

[7]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[8]  Grégoire Toussaint,et al.  Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. , 2003, Magnetic resonance imaging.

[9]  David A. Clausi,et al.  Rapid extraction of image texture by co-occurrence using a hybrid data structure , 2002 .

[10]  John N. Lygouras,et al.  The usage of soft-computing methodologies in interpreting capsule endoscopy , 2007, Eng. Appl. Artif. Intell..

[11]  F. Girosi,et al.  Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification , 1993 .

[12]  V.-E. Neagoe,et al.  Concurrent Self-Organizing Maps for Multispectral Facial Image Recognition , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.

[13]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[14]  James Michael Coggins,et al.  A framework for texture analysis based on spatial filtering , 1983 .

[15]  L SwetsDaniel,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996 .

[16]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[17]  Konstantina S. Nikita,et al.  Computer aided diagnosis based on medical image processing and artificial intelligence methods , 2006 .

[18]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Du-Yih Tsai,et al.  Measurements of texture features of medical images and its application to computer-aided diagnosis in cardiomyopathy , 2005 .

[20]  John N. Lygouras,et al.  A neuro-fuzzy-based system for detecting abnormal patterns in wireless-capsule endoscopic images , 2007, Neurocomputing.

[21]  Carlos E. Thomaz,et al.  Extracting Discriminative Information from Medical Images: A Multivariate Linear Approach , 2006, 2006 19th Brazilian Symposium on Computer Graphics and Image Processing.

[22]  A. Kandaswamy,et al.  Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms , 2007, Comput. Medical Imaging Graph..

[23]  M.H. Mohamed,et al.  An efficient clustering based texture feature extraction for medical image , 2008, 2008 11th International Conference on Computer and Information Technology.

[24]  Osmar R. Zaïane,et al.  Application of Data Mining Techniques for Medical Image Classification , 2001, MDM/KDD.

[25]  Wanjiku Ng'ang'a,et al.  Semantic analysis of Kiswahili words using the Self-Organizing Map , 2003 .

[26]  D. Hansell,et al.  Obstructive lung diseases: texture classification for differentiation at CT. , 2003, Radiology.

[27]  G. Clark,et al.  Reference , 2008 .

[28]  Johan Montagnat,et al.  Texture based medical image indexing and retrieval: application to cardiac imaging , 2004, MIR '04.

[29]  Jyotika Pruthi,et al.  Computer Aided Diagnosis Based on Medical Image Processing and Artificial Intelligence Methods , 2013 .