22-06-2012, 03:32 PM
3D EM/MPM MEDICAL IMAGE SEGMENTATION
USING AN FPGA EMBEDDED DESIGN IMPLEMENTATION
[attachment=25097]
INTRODUCTION
In recent years, three dimensional images are becoming more and more popular
due to advanced visualization techniques. In the medical area, computed tomography
(CT) and magnetic resonance (MR) are widely used for patient diagnosis. Because it
is difficult for doctors to diagnose and make a treatment plan using only 2D images,
3D image diagnostic equipment such as 3D and 4D ultrasound are widely used. For
tissues surrounded by layers of different texture in some hidden angle, segmented
3D images in the visualization can improve clinical understanding. Therefore, fast
image segmentation is the first step for a good visualization. This thesis describes
the acceleration of 3D image segmentation.
Expectation-Maximization
Expectation Maximization (EM) is an iterative procedure for estimation of the
mean and the variance of each segmentation classes. At each iteration, two steps
are performed: the expectation step and the maximization step. First maximization
step is performed, then the segmentation is done in the expectation step, iterating
to find the best log-likelihood of the probability that a particular pixel belongs to
3D Maximization of Posterior Marginals
At each pixel, the MPM optimization uses the Gaussian distribution of each class
and the class probability of the neighborhood pixels. As in the literature [1, 2, 4], the
3D pixel neighborhood is defined by the function t (xr, xs), where xs is the center
pixel to be assigned, and xr are the nearest 6 pixel neighbors: up, down, left, right,
next slice, previous slice. This neighborhood prior probability is defined below as the
probability of the segmentation choice of x, given the segmentation choices of the
neighbors.
The Structure and Main Work Steps of This System
This system consists of a host PC and an external FPGA development board
(Xilinx ML605). The host PC sends 3D image data to the FPGA development board
through the PCI-Express bus. Then, after FPGA hardware completes the EM/MPM
image segmentation processing, the FPGA sends the new 3D image data back to PC.
Figure 3.1 shows the system structure; and the following are the working steps of this
system:
MicroBlaze Embedded Processor
The MicroBlaze Embedded processor soft core is a reduced instruction set computer
(RISC) optimized for implementation in Xilinx Field Programmable Gate Arrays
(FPGAs). It is highly configurable and parameterized to allow selective enabling
of additional functionality [10]. In this system, the processor is responsible for the
overall coordination of all the components. It calculates new means and variances
by using EM algorithm and moves data to and from MPM logic through a Processor
Local Bus (PLB).
USING AN FPGA EMBEDDED DESIGN IMPLEMENTATION
[attachment=25097]
INTRODUCTION
In recent years, three dimensional images are becoming more and more popular
due to advanced visualization techniques. In the medical area, computed tomography
(CT) and magnetic resonance (MR) are widely used for patient diagnosis. Because it
is difficult for doctors to diagnose and make a treatment plan using only 2D images,
3D image diagnostic equipment such as 3D and 4D ultrasound are widely used. For
tissues surrounded by layers of different texture in some hidden angle, segmented
3D images in the visualization can improve clinical understanding. Therefore, fast
image segmentation is the first step for a good visualization. This thesis describes
the acceleration of 3D image segmentation.
Expectation-Maximization
Expectation Maximization (EM) is an iterative procedure for estimation of the
mean and the variance of each segmentation classes. At each iteration, two steps
are performed: the expectation step and the maximization step. First maximization
step is performed, then the segmentation is done in the expectation step, iterating
to find the best log-likelihood of the probability that a particular pixel belongs to
3D Maximization of Posterior Marginals
At each pixel, the MPM optimization uses the Gaussian distribution of each class
and the class probability of the neighborhood pixels. As in the literature [1, 2, 4], the
3D pixel neighborhood is defined by the function t (xr, xs), where xs is the center
pixel to be assigned, and xr are the nearest 6 pixel neighbors: up, down, left, right,
next slice, previous slice. This neighborhood prior probability is defined below as the
probability of the segmentation choice of x, given the segmentation choices of the
neighbors.
The Structure and Main Work Steps of This System
This system consists of a host PC and an external FPGA development board
(Xilinx ML605). The host PC sends 3D image data to the FPGA development board
through the PCI-Express bus. Then, after FPGA hardware completes the EM/MPM
image segmentation processing, the FPGA sends the new 3D image data back to PC.
Figure 3.1 shows the system structure; and the following are the working steps of this
system:
MicroBlaze Embedded Processor
The MicroBlaze Embedded processor soft core is a reduced instruction set computer
(RISC) optimized for implementation in Xilinx Field Programmable Gate Arrays
(FPGAs). It is highly configurable and parameterized to allow selective enabling
of additional functionality [10]. In this system, the processor is responsible for the
overall coordination of all the components. It calculates new means and variances
by using EM algorithm and moves data to and from MPM logic through a Processor
Local Bus (PLB).