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a combined method of fractal and

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Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.4, August 2014 DOI : 10.5121/sipij.2014.5409 85  A  COMBINED METHOD OF FRACTAL  A ND GLCM FEATURES FOR  MRI  A ND CT SCAN IMAGES CLASSIFICATION Redouan Korchiyne l, 2 , Sidi Mohamed Farssi 2 , Abderrahmane Sbihi 3 , Rajaa Touahni 1 , Mustapha Tahiri Alaoui 2  1  LASTID Laboratory, Ibn Tofail University- Faculty of Sciences, Kenitra, Morocco 2  Laboratory of medical Images and Bioinformatics, Cheikh Anta Diop University- Polytechnic High School, Dakar, Senegal  3 LTI Laboratory, Abdelmalek Essaadi University-ENSA, Tangier, Morocco  A  BSTRAC T Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture  from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical diagnostics tests for osteoporosis pathologies.  K  EYWOR DS Fractal analysis, GLCM Features, Medical images, Osteoporosis pathologies. 1. INTRODUCTION Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and density which can lead to an increased risk of fracture [1] (see Figure1). Several methods have been applied to characterize the bone texture: (Genetic algorithm) [2] (Multi resolution analysis) [3], hybrid algorithm consisting of Artificial Neural Networks and Genetic Algorithms [4], texture analysis methods (Gabor filter, wavelet transforms and fractal dimension) [5]. Texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity or color. A feature is an image characteristic that can capture certain visual property of the image. Four major methods were used to characterize different textures in an image: statistical methods (co-occurrence method) [6] [7], geometrical (Voroni tessellation features, fractal) [8] [9], model based method (Markov random fields) [10] [11] and signal processing methods (Gabor filters, wavelet transform and curvelets) [7] [11] [12]. The most widely used statistical methods are co- occurrence features [6] [7]. Fractal analysis has been successfully applied in images processing [13]. Applications in medical images are concerned to model the tissues and organ constitutions and analyzing different types [14] [15]. The fractal objects are characterized by: large degree of heterogeneity, self-similarity,

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