CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSING
- 1. BMS COLLEGE OF ENGINEERING BANGALORE 56019, INDIA DEPARTMENTOFELECTRONICSAndCOMMUNICATIONENGINEERING IMAGE PROCESSING PROJECT PRESENTATION CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSING By l kajikho,manish shah,bikram,adnan,sameep
- 2. INTRODUCTION Lung Anatomy The lungs are a pair of sponge-like cone-shaped organs The right lung has three lobes, and is larger than the left lung, which has two lobes Lung tissue transports oxygen to the bloodstream to go to the rest of the body. Cells release carbon dioxide as they use oxygen
- 3. LUNG CANCER Lung cancer is a disease of abnormal cells multiplying and growing into a tumor. Cancer cells can be carried away from the lungs in blood, or lymph fluid that surrounds lung tissue. Lung Cancer Types • Small cell lung cancer • Non small cell lung cancer
- 4. LUNG CANCER DETECTION SYSTEM
- 6. PRE-PROCECSSING IMAGE ENHANCEMENT The image Pre-processing stage starts with 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. In the image enhancement stage we used the following three techniques: Gabor filter Auto-enhancement and Fast Fourier transform techniques.
- 7. GABOR FILTER Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier transform of the Gaussian function. (a) (b) Figure describes (a) the original image and (b) the enhanced image using Gabor Filter.
- 8. FAST FOURIER TRANSFORM Fast Fourier Transform technique operates on Fourier transform of a given image. The frequency domain is a space in which each image value at image position F represents the amount that the intensity values in image “I” vary over a specific distance related to F. Fast Fourier Transform is used here in image filtering (enhancement). Figure given below describes the effect of applying FFT on original images, where FFT method has an enhancement percentage of 27.51%. (a) Original Image (b) Enhanced by FFT
- 9. IMAGE SEGMENTATION Segmentation divides the image into its constituent regions or objects.Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. Image segmentation are of two types: Thresholding approach Marker-Controlled Watershed Segmentation Approach
- 10. THRESHOLDING APPROACH 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. (a) Enhanced image by Gabor (b) Segmented image by thresholding
- 11. MARKER-CONTROLLED WATERSHED SEGMENTATION APPROACH Separating touching objects in an image is one of the more difficult image processing operations. The water shed transform is often applied to this problem. The marker based watershed segmentation can segment unique boundaries from an image. (a) Enhanced image by Gabor (b) Segmented image by Watershed
- 12. FEATURES EXTRACTION AND DETECTION To predict the probability of lung cancer presence, the following two methods are used: Binarization Approach Masking Approach Binarization Approach Binarization approach depends on the fact that the number of black pixels is much greater than white pixels in normal lung images. So count the black pixels for normal and abnormal images to get an average that can be used later as a threshold, if the number of the black pixels of a new image is greater that the threshold, then it indicates that the image is normal, otherwise, if the number of the black pixels is less than the threshold, it indicates that the image in abnormal.
- 13. Fig. Binnarization method procedure Fig. Binarization check method flowchart
- 14. Masking approach Masking approach depends on the fact that the masses are appeared as white connected areas inside lungs The appearance of solid blue colour indicates normal case while appearance of RGB masses indicates the presence of cancer Therefore, combining Binarization and Masking approaches together will lead us to take a decision whethe the case is normal or abnormal
- 15. CONCLUSIONS Lung cancer is the most dangerous and widespread in the world according to stage the discovery of the cancer cells in the lungs. An image improvement technique plays a very important and essential role to avoid serious stages and to reduce its percentage distribution in the world
- 16. THE END