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Vessel detection in X-ray Liver Images

Vessel detection in X-ray Liver Images . Middle Presentation Yanan Liu April 28 , 2009. Motivation. All physicians agree with the obvious conclusion that earlier and more accurate tumor detection would save lives. The sooner a tumor is detected the greater a patient’s chance of survival.

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Vessel detection in X-ray Liver Images

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  1. Vessel detection in X-ray Liver Images Middle Presentation Yanan Liu April 28 , 2009

  2. Motivation All physicians agree with the obvious conclusion that earlier and more accurate tumor detection would save lives. The sooner a tumor is detected the greater a patient’s chance of survival. National Cancer Institute 1999, American Cancer Society 5 year survival rate. Earlier than IIB 75% survival rate

  3. Goal Auto-segement vessels in X-ray liver images for the purpose of vessel detection. vessel segmentation in color images of retina ? X-ray image

  4. Theoretical Background This method uses the 2-D Gabor wavelet coupled with supervised pixel classification in classes vessel and nonvessel . 2-D Gabor wavelet A is a 2×2 positive definite matrix which defines the wavelet anisotropy, B=A-1 and K0 R2 defines the complex exponential basic frequency. The real plane R×R is denoted as R2 and 2-D vectors are represented as bold letters, Eg. X, b,k R2 . The correction term is necessary to enforce the admissibility condition 0

  5. 2-D Gabor wavelet is simply an elongated Gaussian modulated by a complex exponential. The wavelet smoothes the signal in all directions but detects sharp transitions in the direction of k0 In spatial frequencies, the Gabor wavelet is given by a Gaussian function centered at k0 and elongated by e in the ky direction. The Gabor wavelet in the spatial domain (represented by its real part) is shown in The left column, while the frequency Domain counterpart is shown in the right Column. Different configurations of the Parameters are shown to illustrate the Corresponding effects. Light and dark gray Levels correspond to positive and negative Coefficients, respectively. The parameters for the three rows are, From top to bottom: k0 =[2,3],e=1; k0 =[0,3],e=1; k0 =[0,3],e=4.

  6. Supervised Classification In the proposed vessel segmentation approach, image pixels are seen as objects represented by feature vectors, so that statistical classifiers might be applied for segmentation. In this case, each pixel is classified as vessel or nonvessel, using previously trained classifiers. The training sets for the classifiers are derived from manual segmentations of training images: pixels that were manually segmented out are labeled as vessel, while the remaining receive the nonvessel label. Bayesian Gaussian Mixture Model Classifier K-Nearest Neighbor Classifier Linear Minimum Squared Error Classifier

  7. Supervised pixel classification approach The left diagram illustrates the supervised training of a classifier. The trained classifier can then be applied to the segmentation of test images, as illustrated in the right diagram.

  8. Procedure Details1. Split into RGB Components

  9. 2. Pre-processing Image pre-processing for removing undesired border effects. The inverted green channel of the image on the left. The inverted green channel after pre-processing. Presenting the extended border, appears on the right (the original image limit is presented for illustration). The Gabor wavelet responds strongly to high contrast edges, which may lead to false detection of the borders of the camera’s aperture.

  10. 3. Get proper parameters Different scale values of pixels: a=2(left) and a=5(right)

  11. 4. Training the Classifier input training Learned Parameters Result (manual)

  12. 5. Applying classifier on test data test input training Learned Parameters output result

  13. Database: Utrecht DRIVE database (http://www.isi.uu.nl/Research/Databases/DRIVE/) 40 images + Manual segmentations 20 images used to train and 20 to test

  14. Applying this Method on Liver Image LMSE Classifier

  15. Next step 1. Applying this method on more liver images 2. Training different classifiers and Compare the results 3. Advantage and disadvantage of this method

  16. References 1 J. V. B. Soares and R. M. Cesar-Jr. Segmentation of retinal vasculature using wavelets and supervised classification: Theory and implementation. In H. F. Jelinek and M. J. Cree, editors, Automated Image Detection of Retinal Pathology. CRC Press, 2009. To appear. 2 J. V. B. Soares, J. J. G. Leandro, R. M. Cesar-Jr., H. F. Jelinek, and M. J. Cree. Retinal vessel segmentation using the 2-d Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, 25:1214-1222, 2006

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