DOI: http://dx.doi.org/10.26483/ijarcs.v10i1.6367 Volume 10, No. 1, January-February 2019 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info © 2015-19, IJARCS All Rights Reserved 58 ISSN No. 0976‐5697 FEATURES EXTRACTION IN 3D IMAGES VISUALIZED IN HEXAGONAL LATTICE OF Z 3 GRID Mohd. Sherfuddin Khan Research Scholar,Department of Electronics, G.H Raisoni College of Engineering, Nagpur, Maharashtra, India Vibha Bora Professor & Head Electronics Dept. G.H Raisoni College of Engineering, Nagpur, Maharashtra, India Vijay H. Mankar Head of E&TC Government Polytechnic Ahmednagar, Maharashtra, India Abstract: visualizing and processing Three Dimensional Digital images to Extract features in 3D images has become a subject of interest now-a days as many researchers are still processing and visualizing 3D digital images using 2.5D algorithms rather than 3D algorithms which is imprecise . and the implicit arrangement of human visual sensor array is hexagonal in nature .this intended many people including researchers and scientist for a long time to process the digital image over hexagonal prism lattice to extract better curvilinear features when compared to image processed over Rectangular lattice . in This paper we propose a simulated hexagonal prismatic lattice .and the hexagonal image Algebra for Extracting features processed on hexagonal prism lattice using algorithms developed in the framework of CLAP and 3D morphology . and real time 3D MRI images for testing the algorithms has been demonstrated along with sectioned view for visual inspection of linear and non linear features. Keywords: Mathematical Morphology; Volumetric Features; Surface Detection; 3-D Image Processing 1. INTRODUCTION Description of an images in dissimilar scales and views applicable , to the need of the user is utmost significance and the present situation in visualization based systems. This will help the researchers to extract various features in directions which will then improve the division and segmentation of the images. In this juncture, Sima and Kay[1] put forward off-nadir parts which are given less importance and also not considered earlier. Such a way has provided efficient optimization in rendering. Rather than using the excess data, only the data which is near viewing imagery data is taken as input against those pixels with an maximized geometry for the process of rendering. This pixel which is maximized is further used as the pixel that corresponds to the to see angle that is most orthogonal that models the actual surface of the object[1].For efficient understanding of morphology of a three dimensional structure range images are required, which provide the basic necessity . An efficient edge detection algorithm must be able to extract features in such a way that they are linear in range as well as intensity of the image data. Most of the edge detection algorithms cannot detect edges appropriately in the presence of noise as they will be focusing on synthetic range images. Hence, Alshawabkeh[2] proposed an efficient edge detection algorithm which is mathematically efficient that can better localize edge pixels and is also robust to noise. Surface reconstruction is an interpolation problem which uses different types of methods with three angle in many of the algorithms that shows reconstruction. These surfaces can be smoothly extracted by the use of a bounding tetrahedral[3]. The surfaces[11] of an object shows the shape of the object. Skeleton is such type of methods which detects the shape of the object. The three dimensional shape of an object is the curved skeleton which is of one dimensional skeleton. Silhouette is the output that is derived from a curve skeleton. Such a skeletonisation is a difficult and hence it can be thought of as a maximization problem[4].For perfect data coincidence least squares matching is commonly preferred, which is also the best mathematical model. The well- known ones are LS image matching which is done in two dimensional pixel space and LS multiple cuboid matching which is done in three dimensional voxel space. This method is viewed to have a computational complexity of O(N 2 ) and the ability of handling multi-sensor, temporal, resolution and scale data[5].Generally, for non-invasive type of medical surgeries the visualization algorithms will be of great help. The surface visualization must be good enough such that the doctor can easily make a note of the presence of pathologies and polyps. In such a case three dimensional visualization will aid more to determine the setup perfectly[6]. Hence, here we worked improving a three dimensional edge extraction which has resulted in the better visualization that