In Vivo Reflectance-mode Confocal Microscopy in Clinical Dermatology and Cosmetology
Automatic in vivo Microscopy Video Mining for Leukocytes
-
Upload
zachary-rivas -
Category
Documents
-
view
25 -
download
2
description
Transcript of Automatic in vivo Microscopy Video Mining for Leukocytes
Automatic in vivo Microscopy Video Mining for Leukocytes
* Chengcui Zhang, Wei-Bang Chen, Lin Yang, Xin Chen, John K. Johnstone
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
Objectives
Given a sequence of in vivo images, Track the moving leukocytes Calculate their average velocity Find the magnitude of adherent leukocytes
Challenges
Server Noise Background movement
Due to movement of the living organism Deformation of leukocytes Change of contrast in different frames
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
Suggested Approach
Three main steps:
1. Frame Alignment• To correct the camera/subject movement
2. Detect Moving Leukocytes
3. Detect Adherent Leukocytes• After moving leukocytes are removed
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
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
Step 1- Frame Alignment
• For every matched point x and x’
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
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
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
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
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
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
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