Geometric Active Contour Detection using Gradient Vector
Flow and Shape-Based Image Segmentation
ABSTRACT:
In computer vision and pattern recognition, the role of image processing for image
segmentation or object boundary recognition plays a key role. In traditional snake algorithm
the boundary of image is considered as parametric curve. The process of finding an object
boundary has become an energy minimization process. In the project work a combined process
of GVF Snake algorithm which has larger capture range and stronger convergence ability to
boundary concavities than traditional snake and SUSAN approach has been implemented. The
Corner points at the edge are checked first using Susan approach and then those points are
marked as energy minimization points, then GVF Snake model has been used to capture object
boundary after set initial snake curve. The results obtained indicate that the combined
approach of SUSAN and GVF Snake algorithms based segmentation process and further building
and specialized object recognition system , which is being training by segmented images
obtained by this approach can improve GVF snake model's precision to capture the boundary
with sharp angled corner as well as Object recognition system.
Key-Words: GVF, Snake, SUSA, Active Contour Models
INTRODUCTION:
Active contours, or snakes, are computer-generated curves that move within images to find
object boundaries. They are often used in computer vision and image analysis to detect and
locate objects, and to describe their shape. Problems associated with initialization and poor
convergence to boundary concavities, however, has limited their utility. This research work
presents a new external force for active contours, largely solving both problems. This external
force, which we call gradient vector flow, is computed as a diffusion of the gradient vectors of a
gray-level or binary edge map derived from the image. In this research work, a new image
segmentation algorithm based on susan approach and gradient vectors Snake model is
proposed. First, the comer points at the edge is checked using susan approach and mark them
as energy minimization points, then use gradient vectors Snake model to capture object
boundary after set initial snake curve. The major target behind this research work is to show
that the new algorithm can improve gradient vectors snake model's preCISIOn to capture the
boundary with sharp-angled comer.
After observing the various flaws in the traditional process of image processing, the proposed
system highlights about the new image segmentation algorithm based on susan approach and
gradient vector flow Snake model. First the comer points at the edge using Susan approach and
will be marked as energy minimization points, then by using gradient vector flow Snake model
VEDLABS, #112, Oxford Towers, Old airport Road, Kodihalli, Bangalore-08
www.vedlabs.com , Email id: projects@vedlabs.com, Ph: 080-42040494.
Page 1
object boundary will be captured after setting initial snake curve. The Algorithm should improve
gradient vector flow snake model's precision to capture the boundary with sharp and
complicated image.
BLOCK DIAGRAM:
Figure 1. Data Flow Diagram for GVF and SUSAN.
The project work is designed along with the recognition of the objects using a specific modeling.
The application accepts the image input and can be subjected to various active contour
detection system based on GVF, SUSAN, traditional, as well as amalgamation of both GVF along
with SUSAN algorithm. In the framework, a circular mask having a centre pixel which shall be
known as the nucleus is displayed in the framework along with the image. If the brightness of
each pixel within a mask is compared with brightness of that mask's nucleus than an area of the
mask can be defined which has the same brightness as the nucleus.
VEDLABS, #112, Oxford Towers, Old airport Road, Kodihalli, Bangalore-08
www.vedlabs.com , Email id: projects@vedlabs.com, Ph: 080-42040494.
Page 2
Figure 2. Overview of Architecture
The project architecture is decided to design with an input of a image. There processing of the
image is done by two mode in parallel. In the 1 st mode, the input image is checked for the
comer points at the edge deploying susan approach. Then the comer points is marked as energy
minimization points. In the 2nd mode, for the input image,m the external energy is calculated
and then an energy minimization point is evaluated. The project architecture in brief is
highlighted in Fig 2.
HARDWARE AND SOFTWARE REQUIREMENTS:
Software Requirement Specification:

Operating System: Windows XP with SP2

Tool: MATLAB R2010, Version: 7.10.0
Hardware Requirement specification:

Minimum Intel Pentium IV Processor
VEDLABS, #112, Oxford Towers, Old airport Road, Kodihalli, Bangalore-08
www.vedlabs.com , Email id: projects@vedlabs.com, Ph: 080-42040494.
Page 3

Primary memory: 2 GB RAM,
REFERENCES:
[1] lin yong Cheng [I] and Caixia Liuwe, Image Segmentation with GVF Snake and Comer
Detection, Proceedings of the 2008 International Conference on Computer Science and
Software Engineering - Volume 01, 2008
[2] Gozde Unal, Shawn Lankton, Stephane Carlier, Greg ,Slabaugh,Yunqiang Chen, "Fusion of
IVUS and OCT Through Semi-Automatic Registration," The First International Workshop on
Computer Vision for Intravascular Imaging (CVII), held in conjunction with MICCAI 2006.
[3] Syed Saqib Bukhari, Coupled Snakelet Model for Curled Textline Segmentation of CameraCaptured Document Images, Proceedings of the 2009 10th International Conference on
Document Analysis and Recognition, 2009
[4] V Srikrishnan, Stabilization of parametric active contours using a tangential redistribution
term, IEEE Transactions on Image Processing, 2009
[5] Yogesh Rathi, Tracking Deforming Objects Using Particle Filtering for Geometric Active
Contours, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
VEDLABS, #112, Oxford Towers, Old airport Road, Kodihalli, Bangalore-08
www.vedlabs.com , Email id: projects@vedlabs.com, Ph: 080-42040494.
Page 4