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