Transcript 58-61-ET-V1-S1__spec..

SPECKLE REDUCTION,
SEGMENTATION AND REGISTRATION
IN MEDICAL IMAGES : A
COMPARATIVE STUDY
By
Abhinav Kumar Kushwaha
Alok Ranjan
Supervisor
Dr. Rajeev Srivastava
ABSTRACT
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Speckle noise is a ubiquitous artifact that limits the
interpretation of optical coherence tomography images and
ultrasound images. Here we apply various speckle-reduction
digital filters to OCT and US images and compare their
performances. Our results indicate that adaptive filters,
enhanced Lee, Frost and Wiener filters can significantly reduce
speckle and increase the signal-to-noise ratio, while preserving
strong edges.
In computer vision, segmentation refers to the process of
partitioning a digital image into multiple segments (sets of
pixels, also known as super pixels). The goal of segmentation is
to simplify and/or change the representation of an image into
something that is more meaningful and easier to analyze.
Image registration is the process of overlaying images of the
same scene taken at different times, from different viewpoints,
and/or by different sensors. The registration geometrically
aligns two images (the reference and sourced image).
ABSTRACT….
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Here, we present a tool which can perform speckle
reduction, segmentation and registration all using several
methods and then compare the results obtained.
KEYWORDS: Speckle Reduction, Medical Image, Image
Segmentation, Registration, Ultrasound Image, Image
Processing, SRAD filter, Lee Filter, Frost Filter, PDE based
filter, Locally affine, FFT based algorithm, Tool design,
Performance Comparison.
SPECKLE PATTERN
Random intensity pattern produced by the
interference of a set of wave fronts
 This phenomena has been investigated by scientists
since the time of Newton
 An illuminated surface acts as a source of secondary
spherical waves
 At any point in this scattered light field, the light is
made up of waves of different path lengths and the
resultant wave varies randomly
 If light of low coherence is used, a speckle pattern
will not be normally observed
 Speckle patterns can be observed in polychromatic
light
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SPECKLE REDUCTION
Original Holographic image (left) and Speckle reduced image (right)
SPECKLE REDUCTION
Original Ultrasound image (left) and Speckle reduced image (right)
SEGMENTATION
Segmentation refers to the process of partitioning
a digital image into multiple segments.
 More precisely, image segmentation is the
process of assigning a label to every pixel in an
image such that pixels with the same label share
certain visual characteristics.
 Each of the pixels in a region is similar with
respect to some characteristic or computed
property, such as color, intensity, or texture.
 The result of image segmentation is a set of
segments that collectively cover the entire image,
or a set of contours extracted from the image.
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EXAMPLE OF TEXTURE BASED
SEGMENTATION
REGISTRATION
Fusion, Superimposition and Matching
 A process of finding a transformation that aligns
one image to another
 Goal of registration is to find a correspondence
function or mapping M(.) that takes each spatial
co-ordinate X(s) for source image and returns a
co-ordinate X(t) for the target image
 Used in comparing images to find tumor growth,
target tracking in defense, face/thumb
recognition in security
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IMAGE REGISTRATION
METHODS USED FOR IMAGE
REGISTRATION
Locally Affine Based
 Projective Based
 Fast Fourier Transform Based
 MIRT tool
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LOCALLY AFFINE BASED
In the first stage, the transformation is
formulated as being purely affine.
 In the second stage, this purely or affine
geometric transform is extended to also implicitly
account for contrast and brightness modulations.
 Finally, in third stage, a smoothness constraint is
imposed on all locally estimated geometric and
intensity parameters.
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PROJECTIVE BASED
First we calculate vertical and horizontal
derivatives between the two images.
 From
these derivatives, we estimate an
approximate model of the projective parameters
such as bilinear.
 Calculate
the new coordinates from the
approximate model
 These old and new coordinates now completely
determine the projective parameters in the exact
model
 These new parameters are now applied to one of
the images and iterate till negligible difference
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FAST FOURIER TRANSFORM BASED
This method relies on the translation property of
the Fourier transform referred to as Fourier Shift
theorem.
 By taking inverse Fourier Transform of the
representation in the frequency domain, we have
a function which is approximately zero
everywhere except at the displacement that is
needed to optimally register the two images.
 This method shows excellent robustness against
random noise.
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MIRT
MIRT is a Matlab software package for 2D nonrigid image registration.
 The geometric transformation is based on cubic
B-splines.
 The optimization is based on Euler Method
(gradient-based).
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RESULTS
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Locally Affine Based
RESULTS
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Projective Based
RESULTS
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FFT based
RESULTS
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MIRT
RESULTS
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Target image is obtained by adding brightness to
the source image and comparison has been done
between locally affine based and MIRT.
RESULTS
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Target image has been obtained by varying the
contrast in the source image and comparison has
been done between locally affine based and
MIRT.
RESULTS
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Target image is obtained by scaling the source
image by varying factor and the behavior of FFT
based algorithm has been observed.
RESULTS
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Target image is obtained by rotating the source
image by varying the angle and the behavior of
projective based algorithm has been observed.
CONCLUSION
Performance of MIRT is better when variations
are performed on brightness and contrast of the
target image whereas FFT and projective based
approach performs very poor.
 FFT based approach has better results when
scaling has been performed on target image.
 When target image is rotated by different angles
then projective based approach has best results
than other three approaches.
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REFERENCES
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THANKS