Heart attack and stroke are the major causes of human death in the world and industrial countries, because of cardiovascular diseases. This kind of death is more frequent than all forms of cancer in my country. One of the most commonly used diagnostic coronary artery disease tools is intravascular ultrasound (IVUS) technique. IVUS imaging is more accurate than coronary angiography and it is becoming a well-known imaging technique for direct visualization of coronary arteries. However, visual evaluation and characterization of plaque require integration of complex information and suffer from substantial variability depending on the observer. This fact explains the difficulties of manual segmentation prone to high subjectivity in final results. Therefor, automatic segmentation will save time and provides objective vessel measurements. Moreover IVUS image segmentation is the main step of measurement analysis. So accuracy of IVUS segmentation (i.e., segmentation of lumen, intima, plaque, and wall borders) is the most important factor in quantitative analysis and it is a prerequisite for quantitative analysis.Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its plaque shape reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. Detection of the vessel wall in IVUS images has been approached in several recent works. In Chun Yang and et al. work, a combining IVUS imaging, computational modeling, angiography, and technical testing is proposed to perform mechanical analysis for human coronary atherosclerotic plaques for potential more accurate plaque vulnerability assessment. Gozde Unal et al. present a shape-driven approach to segmentation of the arterial wall from IVUS images in the rectangular domain. M.H. Roy Cardinal and Meunier present a segmentation method based on the fast-marching method which uses gray level probability density functions (PDFs) of the vessel wall structures. Although the previous methods in IVUS image segmentation that explained are usually hampered by noise and artifacts on the IVUS images and in the dark shadow and soft plaque region have some inaccuracy, but our method is robust against this problem. In this thesis we use a novel method based on texture method, which is a texture feature extractor based on "gray-level co-occurrence matrices" patterns. Since it is a fundamental property of texture, it can segment IVUS images in a high level accuracy. We present a texture approach for detection of plaque wall from intravascular ultrasound (IVUS) images. In a properly built shape of plaque using texture feature extraction, we constrain the internal lumen and external lumen borders, so intimae zone can reconstructed. We utilized data coming from IVUS probes at both 20 and 40 MHz, from which our database is provided. This database is input for a post processing stage which can calculate measurements such as lumen and intimae size, plaque shape and hard plaque size. The proposed method contains some stages such as; polar transform, gray level co-occurrence matrix (GLCM), fuzzy C-means (FCM) clustering, morphological processing and curve fitting function. In this thesis we detect soft and hard plaque separately. Keywords: Intravascular Ultrasound, Plaque Detection, Feature extraction, GLCM