Tıp alanında kanserli hücrelerin tespiti @ hasan abdi
-
Upload
hassan-k-abdi -
Category
Education
-
view
256 -
download
5
description
Transcript of Tıp alanında kanserli hücrelerin tespiti @ hasan abdi
TIP ALANINDA KANSERLI HÜCRELERIN TESPITI
Detection of cancer cells in the medical field
Hassan-k A MohaIstanbul University
Computer Engineering
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
Introduction 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.
Medical Imaging TechnologiesTıbbi Görüntüleme Teknolojileri
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)
The Role of Imaging in Cancer Care
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
The Role of Imaging in Cancer Care
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
Lung cancer Detection Approach
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
Image Enhancement
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.
Image Enhancement
Image enhancement stage we use the following three techniques: Gabor filter, Auto-enhancement Fast Fourier transform techniques
Gabor Filter
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.
Fast Fourier TransformFast Fourier Transform technique operates on Fourier transform of a given image. Fast Fourier Transform is used here in image filtering (enhancement).
Comparison image enhancement techniques
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
Image Segmentation
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 2DThe goal of segmentation is to simplify and/or change the representation of the image into something that is more meaningful and easier to analyse.
Thresholding approach( eşikleme yaklaşım)
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
Thresholding approach
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.
Features ExtractionÖzellikler Ekstraksiyon
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
Binarization Approach
Binarization approach depends on the fact that the number of black pixels is much greater than white pixels in normal lung images,
Masking Approach
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
Masking Approach
Figure 8 shows normal and abnormal images resulted by implementing Masking approach using MATLAB
Masking Approach
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.
CONCLUSİON