Detection of nerves in Ultrasound Images using edge detection techniques NIRANJAN TALLAPALLY

Problem statement Problem Description Some of the papers (Edge Detection) reviewed Canny Edge Detection Detection of images in speckle images Authentication of edges produced by zero crossings Adaptive Transform edge detection Summary Outline

Image Segmentation in Ultrasound imaging Ultrasound Imaging: In Ultrasound imaging, high frequency sound waves are used to capture the images. Usually sound waves are directed towards object to be imaged. From the sound waves reflected from the object, 2-D image is constructed. Problem Statement: To identify nerves and enhance them in ultrasound images. Problem Description: Usually nerves enclose blood vessels and they can be easily differentiated. But when nerves are close to bone both are mapped as bright regions in ultrasound image. Hence difficult to differentiate Approach: To apply existing algorithms and see how they are performing. Modifying the algorithm according to requirements Testing the results by comparing with a known data set

Figure: Ultrasound Image

Figure: Detection of nerves in the region of interest (Segmentation) Nerves Blood Vessels Boundary between muscles

Canny edge detection 1 Flowchart for Canny edge detection. Calculate Local Gradient Non-Maxima-Suppression Hysteresis-Thresholding Image

Canny edge detection 1, cont. Low pass filter the image before calculating the local gradient Suppress the pixels whose gradient magnitude is less than adjacent pixel gradient values – pixel location doesn't correspond to an edge, results in thinning Select two threshold values T1 and T2 if |gradient(f(x,y)| < T1 (non – edge) if |gradient(f(x,y)| > T2 (edge) if T1 < |gradient(f(x,y)| <T2 (edge if pixel got a path to pixel whose gradient magnitude is greater than T2 )

Figure 1 : a) Original image b) Image thresholded at T1 c) Image thresholded at 2T1 d) Image thresholded using hysteresis using both thresholds

On Detecting Edges in Speckle Imagery 2 Gaussian filtering and zero cross detection can be combined into single filter- Laplacian of Gaussian Filter( LOG filter) Log filter in case of speckles,multiplicative noise give rise to error edges Trick to suppress the error edges is by using ratio of averages detector along with LOG detector Ratio of averages (RoA) detector RoA(x, y ) = [ H 2 ( x, y ) + V 2 (x, Y ) ] 1/2 > Threshold value H( x, y) = max { R( x,y ) / L( x, y ), L( x, y) / R( x, y ) } similarly V( x, y) R( x, y) and L( x, y) are the average values of the image over neighborhood immediate to the right an d left of coordinate ( x, y) horizontal and vertical So LOG gives all possible edges where as RoA eliminates all the error edges LoG and ROA combined gives valid edges

Authenticating edges produced by zero crossings 3 Zeros in the zero crossing method shows all possible edges It includes both Authentic edges and Phantom edges Authentic edge: An edge of the smoothed intensity function f(x) is authentic edge if the magnitude of the gradient is a maximum Phantom edge: An edge of the smoothed intensity function f (x) is a phantom edge if magnitude of the gradient is a minimum The reason for phantom edge is, the second derivative of a local minimum contains zero crossing (sign transitions). Hence marking it as an edge

Figure 3 : a) 1-D step signal b) The signal after Gaussian smoothening c) The contrast (magnitude of first derivative) Authenticating edges produced by zero crossings 3,cont.

Condition for an edge to be authentic Condition for an edge to be phantom If above inequality is zero then it’s not an edge Similarly for 2-D images one can define condition for phantom and authentic edges If above quantity is negative then it is authentic edge. If it is positive then it is phantom edge. If it is equal to zero then there is no edge Authenticating edges produced by zero crossings 3,cont.

Figure 3 : a) Original Image b) Zero Crossings of the image c) Phantom (white) and authentic edges (black) d) The authentic edges of the image only

Adaptive Transform Edge Detection 4 Image segmentation can be done using transforms such as FFT and DCT The advantage of using the DCT is, it deals with real data – no imaginary part In adaptive scheme filter parameter is tuned automatically to detect edges more precisely The frequency sampled form (FSF) of LOG filter allows the edge locations to be tracked across several filter settings

1-D continuous LOG filter is defined as where Frequency sampled form( FSF ) of LOG filter can be represented as where Adaptive Transform Edge Detection 4

Figure 4 : Flow diagram for edge localization using FSF and the forward and inverse DCT

Adaptive Transform Edge Detection, cont 4. Figure 4 : a) Original image b) Zero crossings (sign transitions) of fixed FSF c) Modified zero crossing using adaptive filter and test criteria d) Noisy image e) and f) are similar to b) and c)

Described problem statement In medical field ( ex : Ultrasound imaging), edge detection plays a role in aiding the physician Described some of the existing edge detection algorithms Would like to use LOG edge detector to identify the edges between berves and any other surrounding regions Summary

1. “A computational approach to edge detection by John Canny”, IEEE Transactions on Pattern Analysis and Machine Intelligence, November “On Detecting Edges in Speckle Imagery”, by Alan C. Bovik,, IEEE Trans. on Acoust. Speech and Signal Proc., Oct “Authenticating Edges produced by Zero-Crossing Algorithms”, by James J.Clark, IEEE Trans. Patt. Anal. Machine Intell, Jan “Algorithms for Adaptive Transform Edge Detection”, by R. Sundaram, IEEE Trans Signal Proc., Aug REFERENCES

QUESTIONS ?