A Framework for a Fully Automatic Karyotyping System. E. Poletti, E. Grisan, A. Ruggeri Department of Information Engineering, University of Padova, Italy. Introduction.
E. Poletti, E. Grisan, A. Ruggeri
Department of Information Engineering, University of Padova, Italy
A cluster is selected for analysis
An axis is extracted andthe SCM is evaluated
Original input image
Pop first cluster
and evaluate the SCM
Push clustersinto the queue
space variant thresholding:cluster identification
The quasi-contact area along adjacent chromosomes.
Geometric analysis and Disentanglement
Resolution of the cluster used as example
Concave points identification
OverlapsEach two of lines connecting disjoint pairs of minima points in K are considered.
Concave points as cues
The local minima of the curvature of the contour (K) are the points suggesting the possible presence of touching and overlaps.
Candidate cut lines links two points in K and lies entirely inside the cluster.
Concave points are here identified and used as cues for cuts and overlaps
Curvature along the contour
Two other geometrical features considered are:
Class Reassigning Algorithm
The human karyotype contains 22 pairs of autosomal chromosomes and 1 pair of sex chromosomes constrained classification problem.
Decision node: specify a predicate condition based on a feature.
Prediction node: specify a value to add to the polarization score.
Axis calculation for the feature extraction
Length distribution for every class,previous (up) and after (down) rescaling
Results and Discussion
The performance of the proposed methods are better or comparable to the best of other methods reported in the literature, providing a tool able to automatically analyze an image, and whose results can be handed over wit minimal human intervention to a classifier for automatic karyotyping.
119 cells containing a total of 5474 chromosomes was analyzed to test the segmentation algorithm. 50 of these cells have been used to train the classifier, 20 to validate the training and 50 to test the classification step.
We have presented an algorithm able to automatically identify chromosomes in metaphase images, taking care of a first segmentation step and then of the disentanglement of chromosome clusters by resolving separately adjacencies and overlaps with a greedy approach, that ensures that at each step only the best split of a blob is performed. The automatic classification step is able to deal with routine images in which chromosomes are randomly rotated, blurred, corrupted by overlapping or by dye stains.
This work has been partially funded by TesiImaging S.r.l., Milan, Italy
Enea Poletti, University of Padova - Dept. of Information Engineering
Via G. Gradenigo 6/a - 35131 Padova - ITALY e-mail: email@example.com