1. An Adept Edge Detection Algorithm for Human Knee Osteoarthritis Images
Submitted by:
Arun V

2. Abstract: 
In this paper, it is discussed the Sobel edge detection operator and its enhanced algorithm.
It is implemented a competent execution time for this new enhanced algorithm to detect edges for human knee osteoarthritis images in different critical situations.
The proposed method is able to exhibit discernible view of salient features of most osteoarthritis images with approximately 50% better execution time compare to classical Sobel method.
Also, it is shown that the algorithm is very effective in case of noisy and blurred images. 

3. Existing System: The traditional edge detection methods are such as
. Log operator
. Sobel operator
. Canny operator
. Gradient operator.
But a large number of digital image processing results show that these pairs of directional edge detection operators are more sensitive, anti-noise ability is poor and generally difficult to obtain satisfactory test results.

4. Proposed system:
In our method, it has been shown edge detection method for knee osteoarthritis images using classical Sobel and proposed an improved modified Sobel algorithm.
The proposed method is able to exhibit discernible view of salient features of most osteoarthritis images with approximately 50% better execution time compare to classical Sobel method.

5. SOBEL OPERATOR:
The Sobel operator is widely used in image processing particularly within edge detection algorithms.
The Sobel operator consists of a pair of 3x3 convolution kernels.
It is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function.

6. SOBEL EDGE DETECTION OPERATOR: Classic Sobel Edge Detection Operator:
The Sobel operator is widely used in image processing, particularly within edge detection algorithms.
Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function.
At each point in the image, the result of the Sobel operator is either the corresponding gradient vector or the normal of this vector.

7. Sobel operator is the partial derivative of f (x, y) as the central computing 3x3 neighbourhood at x, y direction.
In order to suppress noise, a certain weight is correspondingly increased on the centre point.

8. Improved Sobel Edge Detection Operator:
The Sobel operator is based on convolving the image with a small, separable, and integer valued filter in horizontal and vertical direction.
It is relatively inexpensive in terms of computations.
On the other hand, the gradient approximation which it produces is relatively crude, in particular for high frequency variations in the image.

9. As we know, since only the two directions of templates are used, it can only detect the edges of horizontal and vertical direction.
Therefore, the edge detection of this algorithm is ineffective for complicated texture images. The equidistant points have the same weight.

10. Edge detection:
Edges are significant local changes of intensity in an image.
Edges typically occur on the boundary between two different regions in an image.

11. STEPS OF EDGE DETECTION:
Smoothing: suppress as much noise as possible, without destroying the true edges.
Enhancement: apply a filter to enhance the quality of the edges in the image (sharpening).
Detection: determine which edge pixels should be discarded as noise and which should be retained (usually, thresholding provides the criterion used for detection).

12. Localization: determine the exact location of an edge (sub-pixel resolution might be required for some applications, that is, estimate the location of an edge to better than the spacing between pixels). Edge thinning and linking are usually required in this step.

13. IMPLEMENTATION OF PROPOSED ALGORITHM:
The proposed modified Sobel edge detection algorithm identifies both the presence of an edge and the direction of the edge.
The complete program is divided into three major steps stating desaturation, sobel operator and thresholding processes.

14. Steps:
Firstly, to convert an image into gray scale, it is used desaturation process. The RGB values of the images are desaturated (colour removed) and it pushes RGB hues toward gray.
In the IMAGE_SOBEL function it has been calculated gx and gy. After that, it is set minimum-maximum value and used for setting pixel in the image.
After getting the minimum and maximum value, the val has been set. Then, it is multiplied by 255 for making edge white.
Finally the output image has been saved as png format.

15. Table1. Edge detection computational time.

16. Comparison plot diagram of the two algorithms based on
computational time

17. Proposed algorithm output
SCREEN SHOT
Original Image
Sobel output
Proposed algorithm output

18. Conclusion:
This paper has been used to detect edge of medical images particularly knee osteoarthritis pictures in different critical states.
Overcome many shortcomings such as blurring and noise sensitivity.
The implemented program is very efficient at runtime and detecting edges.

19. REFERENCES
Z.Jin-Yu,C.Yan,H.Xian-Xiang,“Edge Detection of Images Based on Improved Sobel Operator and Genetic Algorithms", International Conference on Image Analysis and Signal Processing (IASP 2009), Page(s):31 – April 2009.
Osteoarthritis Research Society International, OARSI, USA.
R. Crane “Simplified Approach to Image Processing” New jersey Prentice hall PTR 1997.

20. THANK YOU