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brain tumor detection by thresholding approach

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brain tumor detection by thresholding approach

  1. 1. Technical Paper TUMOR DETECTION USING THRESHOLD OPERATION IN MRI BRAIN IMAGES(2012,IEEE) Prepared By SAHIL J PRAJAPATI M.E(E.C) 4TH SEM (130370704517)
  2. 2. OUTLINE  Motivation  Abstract  Introduction  Methodology  Work flow  Results  Conclusion
  3. 3. MOTIVATION  Identifying different cancer classes or subclasses with similar morphological appearances present is a challenging problem and has a important implication in cancer diagnosis and treatment.  Present technique includes “Biopsy” procedure which is operative in manner. Classification based on the imaging techniques is not acceptable by the radiologist and oncologist due to required accuracy.  Classification based on gene-expression data has been a powerful method in cancer class discovery.  Thresholding technique was primarily used in detection of tumor but it has a drawback that not all the tumor regions are allocated by this approach so doctors have to use the technique region growing and CAD tool technology.
  4. 4. Abstract Medical image processing is a challenging field now a days and also to process the MRI images because it is the scan of the soft tissues. This Paper focuses on detection of tumor by thresholding approach in which by morphological operation we can be able to detect the tumor region. The Methods include like Preprocessing by sharpening and applying median and mean filters,enhancement is performed by histogram equalization,segmentation is performed by thresholding. Tumor region can be obtaines by using this technique along with image subtraction because some MRI images can be read along with DICOM images. 4
  5. 5. Introduction Tumor is defined as abnormal growth of tissues.Brain tumor is an abnormal mass of tissue in which cells grow and multiply uncontrollably,seeemingly unchecked by the mechanism that control normal cells. Brain tumor can primary and metastatic,also can be benign or maligment. Primary brain tumors include any tumor that start within the brain also it affect the membrane around the brain,nerves or glands. Metastatic brain tumor is a cancer that can spread from elsewhere in the body to any part of the brain. 5
  6. 6. Conti….intro To identify a tumor a patient has to undergo several test but the commonly used test include CT scan,MRI scan,PET scan etc. MRI is used to locate or visualize internal structure of the body in detail.from this detailed anatomical information is collected to examine the human brain develoment and discover the abnormalities. Many different kinds of imaging techniques are used in denoising and visualizing the structure but now a days for classifying the MRI brain images techniques used are-fuzzy logic,neural network,knowledge based methods,variation segmentation. Thresholding is the simplest technique of image segmentation which is used to create binary images from grayscale images,morphological operation is used to check and determine the size and shape of tumor whereas image subtraction is applied to extract tumor region
  7. 7. MRI scan 7 Fig-www.tumorsegmentation.org,www.radiopedia.org
  8. 8. Workflow 8 MRI dataset images Image Preprocessing Preprocessed image Segmentation Morphological operation Texture feature and Selection ClassificationPaper-Tumor detection using threshold operation in MRI brain images ,Natarajan p,Shraiya nancy and pratap singh,2012IEEE
  9. 9. Methodology Gray scale imaging Histogram equalization High pass filter Median filter  threshold segmentation Morphological operation Image subtraction
  10. 10. Grayscale imaging Gray scale imaging is called as black and white image and it can also be called as halftone image sobtained by considering the images as a grid of black dots on white background. Also because there are 8 bits in binary representation of the gray level ,so this method is also called 8- bitgrayscale.also it can be used in the preprocessing step of image segmentation to improve upon the contrasted image. 10
  11. 11. Histogram equalization Histogram are constructed by splitting the range of the data into equal-sized bins (called classes). Then for each bin, the number of points from the data set that fall into each bin are counted. Vertical axis: Frequency (i.e., counts for each bin) Horizontal axis: Response variable. In image histograms the pixels form the horizontal axis In Matlab histograms for images can be constructed using the imhist command. Histogram equalization is a gray level transformation that results in an image may have a flat or peaked histogram.by this global contrast histogram of the image scan be improved.also it accomplishes this by spreading out the most frequent intensity values 11
  12. 12. High pass filter High pass filter is used to do the sharpening of the images to the grayscale images.shapening is used to get the fine details of the image highlighted.also it is used for edge detection. These filters sharpens images by creating a high contrast overlay that emphasis edge in the image ,so also we can say that enhanced image is the result of addition of original image and the scaled version of the line structure and edges in the image. High pass filter is also used to retain the frequency information within the image. 12
  13. 13. Threshold segmentation Segmentation is the process of partitioning the images into multiple segments.(set of pixels). Image segmentation is typically used to locate the objects and boundaries(lines,curves) in the images also we can say assining the label to each pixels in an image such that pixels share same label to view the visual characteristics. Threshold method is based on the threshold value to turn a grayscale image into a binary image. 13
  14. 14. Morphological operation Morphology refers to the description of the properties of the shape and structure of the objects.here binary images consists of various imperfections .thresholding are distorted by the noise and texture featurs. Morphological operations are logical transformation based on the comparision of the pixel neighbourhood with a pattern. These operations are usually performed on the binary images where the pixels values is between 0 and 1. 14
  15. 15. Image subtraction Here in image subtraction operators takes two images as input and produce as output a third image ,whose pixels values are the values obtained by subtraction between the two images. Here in this technique the tumor is extracted based on the closely packed pixels present in the image.by this tumor is removed. 15
  16. 16. Conclusion Morphological operations have proved very helpful in extraction and filtering techniques where operators like open,spur,dilate,erode and close have proved to be helpful in extracting the brain tumor from MRI brain images. Image subtraction technique proved to be helpful along with threshold segmentation to work for the desired region of the image. 16
  17. 17. Results 17
  18. 18. Results conti… 18

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