MATLAB and the Image Processing Toolbox provide a wide range of advanced image processing functions and interactive tools for enhancing and analyzing digital images. The interactive tools allowed us to perform spatial image transformations, morphological operations such as edge detection and noise removal, region-of-interest processing, filtering, basic statistics, curve fitting, FFT, DCT and Radon Transform. Making graphics objects semitransparent is a useful technique in 3-D visualization which furnishes more information about spatial relationships of different structures. The toolbox functions implemented in the open MATLAB language has also been used to develop the customized algorithms.Histogram, 3-D Surface Plot, Round-off Noise Power Spectrum

Emilia Dana SELEŢCHI

English

Research Papers in Economics

MEDICAL IMAGE PROCESSING USING MATLAB 
 
Emilia Dana SELEŢCHI, PhD Candidate 
University of Bucharest, Romania 
 
ABSTRACT 
 
 MATLAB and the Image Processing Toolbox provide a wide range of advanced image processing 
functions and interactive tools for enhancing and analyzing digital images. The interactive tools allowed 
us to perform spatial image transformations, morphological operations such as edge detection and noise 
removal, region-of-interest processing, filtering, basic statistics, curve fitting, FFT, DCT and Radon 
Transform. Making graphics objects semitransparent is a useful technique in 3-D visualization which 
furnishes  more information about spatial relationships of different structures. The toolbox functions 
implemented in the open MATLAB language has also been used to develop the customized algorithms. 
 
Keywords:  Histogram, 3-D Surface Plot, Round-off Noise Power Spectrum  
 
 
1. INTRODUCTION 
 
MATLAB is a high-level technical language and interactive environment for data analysis and 
mathematical computing functions such as: signal processing, optimization, partial differential equation 
solving, etc. It provides interactive tools including: threshold, correlation, Fourier analysis, filtering, basic 
statistics, curve fitting, matrix analysis, 2D and 3D plotting functions. The operations for image 
processing allowed us to perform noise reduction and image enhancement, image transforms, colormap 
manipulation, colorspace conversions, region-of interest processing, and geometric operation [4]. The 
toolbox functions implemented in the open MATLAB language can be used to develop the customized 
algorithms.   
 
2. IMAGE PROCESSING TOOLBOX     
An X-ray Computed Tomography (CT) image is composed of pixels, whose brightness corresponds to the 
absorption of X-rays in a thin rectangular slab of the cross-section, which is called a ’’voxel’’ [1,3]. The 
Pixel Region tool provided by MATLAB 7.0.1 superimposes the pixel region rectangle over the image 
displayed in the Image Tool, defining the group of pixels that are displayed, in extreme close-up view, in 
the Pixel Region tool window. The Pixel Region tool shows the pixels at high magnification, overlaying 
each pixel with its numeric value [2,5]. For RGB images, we find three numeric values, one for each band 
of the image. We can also determine the current position of the pixel region in the target image by using 
the pixel information given at the bottom of the tool. In this way we found the x- and y-coordinates of 
pixels in the target image coordinate system. The Adjust Contrast tool displays a histogram which 
represents the dynamic range of the X-ray CT image (Figure.1.).  
 
  
 
Figure 1. Pixel Region of an X-ray CT scan and the Adjust Contrast Tool 
 
 
The Image Processing Toolbox provide a reference-standard algorithms and graphical tools for image 
analysis tasks including: edge-detection and image segmentation algorithms, image transformation, 
measuring image features, and statistical functions such as mean, median standard deviation, range, etc., 
(Figure. 2.)  
 
 
 
 
Figure 2. Data statistics of an X-ray CT scan performed by MATLAB 7.0.1. 
 
 
3.  PLOT TOOLS 
 
MATLAB provides a collection of plotting tools to generate various types of graphs, displaying the image 
histogram or plotting the profile of intensity values (Fig. 3.a,b).  
  
 
 
Figure 3. a. - The Histogram of X-ray CT image and the plot fits (significant digits: 2). A cubic fitting 
function is the best-fit model for histogram data plot. The fit curve was plotted as a magenta line through 
the data plot. 
 
Area Graph of X-ray CT brain scan displays the elements in a variable as one or more curves and fills the 
area beneath each curve.  
 
 
Figure 3.b. Area Graph of X-ray CT brain scan   
 
 
The 3-D Surface Plot generates a matrix as a surface (Figures 4. a, b, c, d). We can also make the 
faces of a surface transparent to a varying degree. Transparency (referred to as the alpha value) can be 
specified for the whole 3D-object or can be based on an alphamap, which behaves in a way analogous to 
colormaps (Figures 4. a, b).  
  
 
Figure 4.a.  3D Surface Plot of x-ray CT brain scan generated with histogram values, alpha(.0) 
 
 
 
 
Figure 4.b. - 3D Surface Plot of x-ray CT brain scan generated with histogram values, alpha(.4) 
 
 
The ’’meshgrid’ function is extremely useful for computing a function of two Cartesian coordinates. It 
transforms the domain specified by a single vector or two vectors x and y into matrices X and Y for use in 
evaluating functions of two variables. The rows of X are copies of the vector x and the columns of Y are 
copies of the vector y (Figure 4.c).  
 
 
 
Figure 4.c. - 3D Surface Plot of x-ray CT brain scan generated with histogram values, mesh 
 
 
3-D Surface Plot with Contour (Surfc) displays a matrix as a surface with contour plot below. ’’Lighting’’ 
is the technique of illuminating an object with a directional light source. This technique can make subtle 
differences in surface shape easier to see. ’’Lighting’’ processing can also be used to add realism to three-
dimensional graphs. This example uses the same surface as the previous examples, but colors it yellow 
and removes the mesh lines (Figure 4.d.).  
  
 
Figure 4.d. Surface Plot of x-ray CT brain scan generated with histogram values, lightening 
 
 
The ’’Image’’ creates an X-ray CT image graphics object by interpreting each element in a matrix as an 
index into the figure's colormap or directly as RGB values, depending on the data specified (Figure 5.a.). 
 
The ’’Image’’ with Colormap Scaling (’’imagesc’’ function) displays an X-ray CT image and scale to use 
full colormap. MATLAB supports a number of colormaps. A colormap is an m-by-3 matrix of real 
numbers between 0.0 and 1.0. Each row is an RGB vector that defines one color. ,,Jet’’ ranges from blue 
to red, and passes through the colors cyan, yellow, and orange. It is a variation of the hsv (hue, saturation, 
value) colormap (Figure 5.b.).  
 
a)     b)  
Figure 5   
 
Contour Plot  is useful for delineating organ boundaries in images. It displays isolines of a surface 
represented by a matrix (Figure 6.). For example: 
 
 
 
 Figure 6. -  Contour Plot of X-ray CT brain scan  
 
 
The ezsurfc(f) or surfc function creates a graph of f(x,y), where f is a string that represents a mathematical 
function of two variables, such as x and y (Figure 7.).  
 
 
 
Figure 7 – Surfc on X-ray CT brain scan 
 
The contour3 function creates a three-dimensional contour plot of a surface defined on a rectangular grid 
(Figure 8.).  
 
 
Figure 8. – Contour3 on X-ray CT brain scan 
 
 
3-D Lit Surface Plot (Surface plot with colormap-based lighting, surfl function) displays a shaded surface 
based on a combination of ambient, diffuse, and specular lighting models (Figure 9.).  
  
Figure 9. - 3D Lit Surface Plot of X-ray CT brain scan  
 
The 3-D Ribbon Graph of Matrix displays a matrix by graphing the columns as segmented strips (Figure 
10.).  
 
 
Figure 10. - The 3-D Ribbon Graph of X-ray CT brain scan 
 
4. FILTER VISSUALIZATION TOOL (FVTool) 
 
Filter Visualization Tool (FVTool) computes the magnitude response of the digital filter defined with 
numerator, b and denominator, a. By using FVTool we can display the phase response, group delay 
response, impulse response, step response, pole/zero plot, filter coefficients and round-off noise power 
spectrum.(Figures: 11, 12, 13, 14, 15, 16 and 17).  
a)   
 b)  
 
Figure 11. Magnitude and Phase Response - Frequency scale: a) linear, b) log  
a)  
 
b)  
                
 
Figure 12. Group Delay Response - Frequency scale: a) linear, b) log 
 a)   
  
b)  
 
Figure 13. Phase Delay Response - Frequency scale: a) linear, b) log 
 
a)   
 
b)   
Figure 14.  (a) Impulse Response (b) Pole/Zero Plot 
 
 
a)    
 
b)  
 
Figure 15.  Step Response (a) Default (b) Specify Length: 50 
 
a)  
   b)  
 
Figure 16.  Magnitude Response Estimate - Frequency scale: a) linear, b) log 
 
a)   
 
b)  
 
Figure 17.  Magnitude Response and Round-off Noise Power Spectrum 
- Frequency scale: a) linear, b) log 
 
5. CONCLUDING REMARKS 
 
MATLAB provide functions and interactive tools for enhancing and analyzing digital images and 
developing image processing algorithms. MATLAB visualization tools let us to represent the information 
contained in the X-ray CT image data through histograms, contour plots, pixel regions,  transparent 
overlays, and images texture-mapped onto surfaces.  
The Signal Processing Toolbox (SPTool) provides a suite of interactive graphical user interfaces 
for: Signal plotting and analysis, spectral analysis and filter visualization: signal plotting and analysis, 
spectral analysis and filtering signals. 
 
 REFERENCES 
 
[1]. E.G. Bistriceanu - Principiile Matematematice şi Fizice ale Tomografiei Computerizate, Matrix 
ROM, Bucureşti, 1996 
[2]. B. Hahn. D.T. Valentine, Essential MATLAB for Engineers and Scientists, Elsevier (Third 
edition), 2007 
[3]. Webb, S., The Physics of Medical Imaging, Institute of Physics Publishing, London. 
[4]. ImageJ – 1.37 
[5]. MATLAB 7.0.1. – The Language of Technical Computing, The MathWorks, Inc., 2005  

Bistriceanu - Principiile Matematematice şi Fizice ale Tomografiei Computerizate,

Essential MATLAB for Engineers and Scientists, Elsevier (Third edition),

MEDICAL IMAGE PROCESSING USING MATLAB

http://www.rebe.rau.ro/RePEc/rau/jisomg/SU08/JISOM-SU08-A19.pdf

MEDICAL IMAGE PROCESSING USING MATLAB

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