Copyright © 2018 Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (1.1) (2018) 121-124 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper Measuring distinct regions of grayscale image using pixel values S. Jeyalaksshmi 1* , S. Prasanna 2 1 Assistant Professor, Dept of IT, Vels University, Pallavaram, Chennai. 2 Associate Professor, Dept. of Computer Application, Vels University, Pallavaram, Chennai. *Corresponding author E-mail:pravija.lakshmi@gmail.com Abstract Grayscale is a series of shades of gray without apparent color. The total absence of transmitted or reflected light, which is the darkest shade, black. The total reflection or transmission of light at all observable wavelengths, which is nothing but lightest possible shade i.e., white. Intermediate shades of gray are characterized by equal brightness levels of the primary colors (red, green and blue) for transmitting light, or equal amounts of the three primary pigments (magenta,cyan, and yellow) for reflected light. This paper focuses mainly on measuring the properties of objects in a grayscale image using Regionprops to calculate the standard Deviation. This is achieved by segmenting a grayscale image to get objects of a binary image. Although, the common problem of including chromatic values to a grayscale image has objective solution,not exact, the present approach tries to provide an approach to help minimize the amount of human labor required for this task. We transfer the source’s whole color “mood” to the target image by matching texture information and luminance between the images rather than selecting RGB colors from a group of colors to an individual color components. We pick out to transfer only chromatic information and retain the target image’s original luminance values. Further, the procedure is improved by permitting the user to match areas of the two images with rectangular swatches. It is essential to develop grayscale image pixel value, resultant to each object in the binary image to inspect the original grayscale image.Based on the original grayscale image pixel values, the pixel value properties in grayscale image are used to do routine calculations. Keywords: Grayscale image, regionprops, binary image, pixel value. 1. Introduction The colors are extremely subjective and personal. They have a prominent feature by which we try to identify images better and improve the visual appearance of the image. A grayscale image is also known as Black and white, in which the each pixel valuecarries only intensity information. The black and white or grayscale image composed full of gray shades, changing from black at the feeblest intensity (weak) to white at the sturdiest (strong). The fundamental process of color followed by the human brain in perceiving color is a psychological wonder that is not yet fully understood, the color’s physical nature can be expressed on a formal basis supported by experimental and theoretical results. Image analysis involves investigation of the image data for a specific application. Normally, the raw data of a set of images are analyzed to advantage insight into what's going on with the photos and the way they can be used to extract desired information. The processing of an image, recognition of pattern and extraction of feature is a critical step, which is nothing but a distinct form of reduction of dimensionality. When the data(input) are just too large to be processed and alleged to be redundant, then the data is converted into a comprised set of feature depictions. The process of remodeling the input data into a set of capabilities is known as feature extraction. Features regularly comprise information relative to shape, color, context or texture. Basically, the colors we observe in an object are decided by means of the nature of the light pondered from the object. Due to the human eye’s structure, all colors are visible as variable mixtures of the three so-called Primary colors Red, Green and Blue (RGB). Digital image processing permits the use of complicated computer algorithms to carry out image processing digital images. An image is captured through a sensor and digitized. There is a massive representational gap between the image and the concept which describes or abstracts the image data. To connect that gap, image processing has a variety of representations connecting the inputs and the output. Then the Image processing tasks involve in the layout of these intermediate representations and the implementation of algorithms to assemble them and relate them to each other. A Grayscale image is more than a few shapes of gray in obvious color. The darkest color is black and the lightest shade is white. The intermediate shades of gray are represented with the aid of equal brightness level of primarycolors (red, blue, green). Grayscale image may have any value for each pixel between 0 (zero) and 256. A binary image is a digital image that has only two possible intensity values for each pixel. They are commonly displayed as black and white. Binary images are produced with the aid of segmenting the grayscale images containing the objects in the image. 2. Literature review Satyajit Mondal et al.,[1] has proposed a method for similarity measurement of image using the property-regionprops, color and texture. Swain and Ballard[4] has proposed amethod to measure the image similarity by histogram analysis, intersection. Jitendra Malik et.al., has proposed an algorithm for partitioning. There, the