1. Image Processing to quantify vascular data crucial in diagnosis of vascular abnormalities, surgical planning and monitoring tumor progression
Kinsi Oberoi and Bo Sun*
Department of Computer Science & Bioinformatics
Image Processing to quantify vascular data crucial in diagnosis of vascular abnormalities, surgical planning and monitoring tumor progression
Kinsi Oberoi and Bo Sun*
Department of Computer Science & Bioinformatics
Abstract
Methods Results
Background
Conclusion
References
•The two main vascular could be observed with distance
transformation output but further work is needed to get
the small vascular data.
B

A
Characteristic of microscopy vascular data is
that it contains many tiny vessels with branching
and complex structure. The quantification of
these vascular network is crucial in diagnose of
vascular abnormalities, surgical planning, and
monitoring tumor progress or remission.
Therefore, developing an image processing
method to automatically and accurately quantify
the vessel is important. Image processing
toolbox in Matlab provides full power for image
processing applications. In this research study, I
used Matlab to process a digital microscopic
image. The image was first converted to binary
form and then distance transform of binary
image helped in extracting the vascular data. The
result of the transform is a gray level image that
looks similar to the input image, except that the
gray level intensities of points inside foreground
regions are changed to show the distance to the
closest boundary from each point. The main two
vascular were detected by this process but more
work need to be done for extracting small
vascular data.
Display the image :-
Imshow (filename)
Create a Binary Version of the Image :-
BW = im2bw(I,level) converts the grayscale image I
to a binary image. The output image BW replaces all pixels in
the input image with the value 1(white) and replaces all other
pixels with value 0(black), level is in the rang[0,1]. This range
is relative to the signal levels possible for the image's class.
To compute the level argument, you can use the function
graythresh.
Distance Transform :-
D = bwdist(BW) computes the Euclidean distance
transform of the binary image BW. For each pixel in BW , the
distance transform assigns a number that is the distance
between the pixel and the nearest nonzero pixel of BW. The
result is a graylevel image that looks similar to input image,
except that the graylevel intensities of points inside
foreground regions are changes to show the distance to the
closest boundary from each point.
bwdist uses the Euclidean distance metric by
default. BW can have any dimension.D is the same size
as BW.
[D,L] = bwdist(BW) also computes the nearest-neighbor
transform and returns it as label matrix L, which has the
same size as BW and D. Each element of L contains the
linear index of the nearest nonzero pixel of BW.
[D,L] = bwdist(BW,method) computes the distance transform,
where method specifies an alternate distance
metric. method can take any of the following values.
The method string can be abbreviated
Vascular are blood vessels which are part of circulatory
system .They contain many tiny vessels with branching
and complex structure, quantification of these vascular
network is crucial in diagnoses of vascular abnormalities,
surgical planning, and monitoring tumor progress or
remission. Image processing toolbox in Matlab provides
Full power for image processing applications.
MATLAB is a high-performance language for technical
computing. It integrates computation, visualization and
programming in an easy-to-use environment where
problems and solutions are expressed in familiar
mathematical notation.
The basic data structure in MATLAB is the array, an
ordered set of real or complex elements. This object is
naturally suited to the representation of images, real-
valued ordered sets of color or intensity data.
MATLAB stores most images as two-dimensional arrays
(i.e., matrices), in which each element of the matrix
corresponds to a single pixel in the displayed image.
(Pixel is derived from picture element and usually denotes
a single dot on a computer display.)
Figure 1:- Image of mouse ear on which Image
Processing is performed.
Figure 2:- Binary version of Figure 1.
Figure 4:- Reverse Distance transformation
of Figure 3
Figure 3:-Distance transformation of binary
figure 2
Method
Description
'chessboard’
In 2-D, the chessboard distance between (x1,y1) and
(x2,y2) is max(│x1 – x2│,│y1 – y2│).
'cityblock'
In 2-D, the cityblock distance between (x1,y1) and (x2,y2)
is │x1 – x2│ + │y1 – y2│.
'euclidean'
In 2-D, the Euclidean distance between (x1,y1) and (x2,y2)
is
This is the default method.
'quasi-euclidean'
In 2-D, the quasi-Euclidean distance between (x1,y1) and
(x2,y2) is
http://www.mathworks.com/help/toolbox/images/