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Automatic in vivo Microscopy Video Mining for Leukocytes

This paper proposes an automatic video mining approach to track and calculate the velocity of moving leukocytes and detect the magnitude of adherent leukocytes in in-vivo microscopy images. It addresses challenges such as server noise, background movement, and contrast changes. Two approaches, probabilistic learning and neural network, are used for detecting moving leukocytes, and a polynomial fitting method is employed for detecting adherent leukocytes. Experimental results show promising accuracy.

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Automatic in vivo Microscopy Video Mining for Leukocytes

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  1. Automatic in vivo Microscopy Video Mining for Leukocytes * Chengcui Zhang, Wei-Bang Chen, Lin Yang, Xin Chen, John K. Johnstone

  2. Background Information • What is in vivo microscopy? • Images of the cellular and molecular processes in a living organism • Why video-mine leukocytes? • To Predict Inflammatory response • Rolling velocity and magnitude of adhesion of leukocytes are the main predictors • Currently analyzed manually • Time consuming / Expensive • Subjective

  3. Objectives • Given a sequence of in vivo images, • Track the moving leukocytes • Calculate their average velocity • Find the magnitude of adherent leukocytes

  4. Challenges • Server Noise • Background movement • Due to movement of the living organism • Deformation of leukocytes • Change of contrast in different frames

  5. Previous Work • [Eden et al.] use local features (e.g. color) for a tracking system • Assume that leukocytes roll along the vessel centerline • [Acton et al.] Background removal + morphological filter • Assumes the shape/size leukocytes does not change

  6. Suggested Approach • Three main steps: • Frame Alignment • To correct the camera/subject movement • Detect Moving Leukocytes • Detect Adherent Leukocytes • After moving leukocytes are removed

  7. Step 1- Frame Alignment • 1.1- Detect Camera/Subject Movement • Define a (dis)similarity measure between consecutive frames • This allows for some tolerance within radius r • If S(ft-1, ft) is larger than a threshold, then ft requires frame alignment

  8. Step 1- Frame Alignment • 1.2- Frame Matching • Generate a number of high dimensional, local scale-invariant features [SIFT] for the frame and its predecessor • Use nearest-neighbor to find a match for each feature point • Calculate the transformation matrix H, such that • For every matched point x and x’

  9. Step 1- Frame Alignment • Use Random Sample Consensus (RANSAC) to correct the mismatches

  10. Step 2 - Detecting Moving Leukocytes • Approach 1 - Probabilistic Learning • For pixel j in the image, let x1j, x2j, ..., xNj be the intensity of the pixel over N frames • Assume that P(xtj) has a normal distribution over time with mean xtj • If P(xtj) is smaller than a threshold, then it is a foreground pixel • Problem: Difficult to find a threshold

  11. Step 2 - Detecting Moving Leukocytes • Approach 1 - Probabilistic Learning • Problem: Difficult to find the threshold value • Solution: Use One-Class SVM to classify background and foreground pixels

  12. Step 2 - Detecting Moving Leukocytes • Approach 2 - Neural Network • Train a neural net to learn the predictable pattern of the background pixels • Input: [x(t-m), x(t-m+1),... , x(t-1)] • A sliding window of the intensity sequence • Output: x(t) • Prediction for the intensity of the pixel at the next frame • If the neural-net prediction and the real pixel intensity are very different, the pixel in the current frame is in foreground

  13. Step 2 - Detecting Moving Leukocytes • Approach 2 - Neural Network

  14. Step 2 - Detecting Moving Leukocytes • Calculating the leukocytes velocity • Find the centroid of each group of connected foreground pixels • For each centroid, find the closest centroid in the previous frame • If their distance is smaller than a threshold, they are a match • Compute the mean velocity

  15. Step 3- Detecting Adherent Leukocytes • First, remove the moving leukocytes • Three main types of regions left • Tissues • Vessels • Adherent Leukocytes • These three have different intensity values

  16. Step 3- Detecting Adherent Leukocytes

  17. Step 3- Detecting Adherent Leukocytes • Finding the threshold values • Fit an 8th degree polynomial to the histogram curve • The real part of the second largest root is the ideal threshold • Justification? • Problem with false positives and false negatives

  18. Experimental Results • Test video of 148 frames • Detecting moving leukocytes: • 1% false positive for probabilistic learning(?) • 49% false positive for neural-net approach • 50% recall • Detecting Adherent leukocytes • 2% false positive • 95% recall

  19. Final Remarks • Paper is mainly related to Vision • The algorithms require many “magic parameters” that need hand tuning • Would the current parameters work as well for a new video sequence from a new equipment? • Do we want to pursue more video-mining papers?

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