International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 20 (2017) pp. 10169-10175 © Research India Publications. http://www.ripublication.com 10169 A Review Application Of Image Segmentation On Statistical Texture Analysis Sabiq Adzhani Hammam 1 , Tito Waluyo Purboyo 2 and Randy Erfa Saputra 3 1 College Student, Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia. 2 Lecturer, Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia. 3 Lecturer, Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia. Abstract Image processing is becoming very important. A lot of data from an image is then removed, which will be used for identification or coverage. One of image processing technique that is often used today is image segmentation. Image segmentation is used as the first step to separate objects from the background, so the object can be processed for other purposes. Texture analysis is the development of science from image segmentation. The first part of this paper describes the segmentation of images in general, then continued with an explanation of image segmentation in general. Then explain the image segmentation. There are many application could be used for image segmentation based on obtained method: Segmentation On Cancer, Segmentation On Medical Resonance Image, and Segmentation On Texture Batik. The last part of this paper will explain the future work on image segmentation. Keywords: Image Segmentation, Texture Analysis, Medical Resonance Image, Cancer, Batik INTRODUCTION Image segmentation is a stage that was first performed before the image analysis stage in the process of image recognition of a particular input. The function of image segmentation that divides the image of its territories (region) based on the similarity in its kind in the form of textures, colors, shapes, and etc. Implementation of common image segmentation among others can be found in facial recognition application, fruit quality detection, canned food industry, batik industry, and so forth. Many methods are used for image segmentation in texture statistical analysis. From several methods, the application of image segmentation could be applied in many fields like medical image, cancer, and texture. Text -based on images segmentation, grouping it using repeating patterns of pixels in the image. The texture is a function of the spatial variation in pixel intensity in the image. Based on its structure the texture can be divided into macrostructural textures and microstructures. The macrostructural texture has periodic pattern repetition of an area, usually present in man-made patterns and tends to be easily represented mathematically. While the texture of microstructure has a loop that is not clear so it is not easy to provide a comprehensive texture definition. The difficulty in the process of grouping the usual image textures is to define the texture boundaries since the boundaries between textures are often unclear. So the process of segmentation is needed to perform the separation or grouping objects properly so the next result obtained good results. In addition, there are two things that refer to image segmentation based on statistical analysis of textures, namely: (1) Analysis of surface roughness level and (2) pattern structures analysis and orientation. These two things have become the basis for the development of image segmentation based on statistical analysis of textures. The purpose of image segmentation is to recognize patterns of the image. Image segmentation methods need development that is in it to be able to process further using image segmentation based on statistical analysis of texture. By using the development of the results of image segmentation can be implemented in the process of classification and identification of an image. Concept of Texture Image Segmentation In general, the concept of image segmentation is to partition an image into regions or components that do not overlap. These regions should be homogeneous and uniform like Gray Levels and its texture. Segmentation is also commonly done as a first step to implementing object classification. After image segmentation is implemented, the features contained in the image can be taken and classified. The process of segmentation includes two activities: Pre- processing and feature extraction based on statistical methods contained in the texture. This process is a preliminary process to get the feature value contained in the image taken and used for the process of pattern recognition and classification.