Detection of cencer fileds with image processing

2. Presentation Contents
 Introduction
 Medical Imaging Technologies
 The Role of Imaging in Cancer Care
 Lung cancer Detection Approach
 Work Performed and Results
 Image Enhancement
 Gabor Filter
 Fast Fourier Transform
 Image Segmentation
 Thresholding approach
 Features Extraction

3. 
Image processing is one of most growing research area these
days and now it is very much integrated with the medical and
biotechnology field

Cancer is one of the most dangerous disease for which still
proper treatment is not available. World Health Organization
(WHO) mentioned that cancer accounted 13% of all death in
the world in 2004

Cancer is a tumor that grows larger than 2mm in every 3
months and multiplies out of control. It also spreads to other
parts of the body and destroys the healthy tissue.

4. 




X-Rays (X-Işınları)
Mammography (Mamografi )
Ultrasound (Ultrason )
Computed Tomography (Bilgisayarlı Tomografi)
Magnetic Resonance Imaging (Manyetik Rezonans
Görüntüleme)
 Nuclear Medicine (Planar and SPECT Gamma Imaging, PET)
(Nükleer Tıp (Planar ve SPECT Gama Görüntüleme, PET)

5. the role of medical imaging in cancer is
something of an anomaly
On the one hand, imaging plays a vital role in
detecting and treating virtually all types of
cancer

6. The roles are as follows : Imaging Detects Cancer Early
 Imaging Enables Less-Invasive Cancer Diagnosis
and Treatment
 Imaging Fosters More Effective Management of
Cancer
 Imaging Fosters Efficiencies and Savings in Cancer
Care
 Imaging Keeps Workers Productive

7.  Lung cancer is the most dangerous and widespread cancer in
the world according to stage of discovery of the cancer cells in
the lungs
 Lung cancer image processing stages as follows
görüntü yakalama
görüntü geliştirme
görüntü bölünme
Özelikler çıkarma

8. the aim of image enhancement is to improve the
interpretability or perception of information included in the
image for human viewers, or to provide better input for
other automated image processing techniques.
Image enhancement techniques can be divided into two
broad categories:
 Spatial domain methods
 and frequency domain methods.

9. Image enhancement stage we use the following
three techniques:
 Gabor filter,
 Auto-enhancement
 Fast Fourier transform techniques

10. A Gabor filter is a linear filter whose impulse
response is defined by a harmonic function
multiplied by a Gaussian function.
The follwoıng Figure shows a) the original image
and (b) the enhanced image using Gabor Filter.

11. Fast Fourier Transform technique operates on
Fourier transform of a given image.
Fast Fourier Transform is used here in image
filtering (enhancement).

12. Subject
Auto Enhancement
Gabor Filter
FFT Filter
SUB1
37.95
80.975
27.075
SUB2
47.725
80
36.825
SUB3
36.825
79.5
25.625
SUB4
34.775
81.8
25.175
SUB5
32.85
81.4
22.85
Final Average
38.025
80.735
27.51

13. Image segmentation is an essential process for most
image analysis subsequent tasks
Segmentation divides the image into its constituent
regions or objects. Segmentation of medical images in
2D
The goal of segmentation is to simplify and/or change
the representation of the image into something that
is more meaningful and easier to analyse.

14. Thresholding is a non-linear operation that
converts a gray-scale image into a binary image
where the two levels are assigned to pixels that
are below or above the specified threshold value

15. Threshold values based on this method will be
between 0 and 1, after achieving the threshold
value; image will be segmented based on it. Figure
4 shows the result of applying thresholding
technique.

16. Image features Extraction stage is an important
stage that uses algorithms and techniques to
detect and isolate various desired portions or
shapes (features) of a given image.
To predict the probability of lung cancer
presence, the following two methods are used:
binarization and masking

17. Binarization approach depends on the fact that
the number of black pixels is much greater than
white pixels in normal lung images,

18. Masking approach depends on the fact that the
masses(means areas affected by cancer) are
appeared as white connected areas inside ROI
(lungs), as they increase the percent of cancer
presence increase.
The appearance of solid blue colour indicates
normal case while appearance of RGB masses
indicates the presence of cancer

19. Figure 8 shows normal and abnormal images
resulted by implementing Masking approach using
MATLAB

20. Combining Binarization and Masking approaches
together will lead us to take a decision whether
the case is normal or abnormal according to the
mentioned assumptions in the previous two
approaches.