A Framework for a A Framework for a Fully Automatic Karyotyping SystemFully Automatic Karyotyping System

E. Poletti, E. Grisan, A. Ruggeri

Department of Information Engineering, University of Padova, Italy

IntroductionIntroduction

Methods: SegmentationMethods: Segmentation

AcknowledgementsAcknowledgementsThis work has been partially funded by TesiImaging S.r.l., Milan, Italy

CorrespondenceCorrespondenceEnea Poletti, University of Padova - Dept. of Information Engineering Via G. Gradenigo 6/a - 35131 Padova - ITALY   e-mail: enea.poletti@dei.unipd.it

Results and DiscussionResults and Discussion

Karyotype analysis is a widespread procedure in cytogenetics to assess the possible presence of genetics defects. The procedure is lengthy and repetitive, so that an automatic analysis would greatly help the cytogenetist routine work.

Still, automatic segmentation and classification of chromosomes are open issues: existing commercial software packages are far from being fully automatic and their poor performances require human intervention to correct challenging situations. We propose a framework for a fully automatic karyotyping procedure.

space variant thresholding:cluster identification

Original input imagePush clustersinto the queue

Pop first cluster and

evaluate the SCM

Single chromosome?

Save singlechromosome

A cluster is selected for analysis

An axis is extracted andthe SCM is evaluated

Concave points identification

Concave points are here identified and used as cues

for cuts and overlaps

Curvature along the contour

Resolution of the cluster used as example

Identify newclusters

Geometric analysis and

Disentanglement

Concave points as cuesThe local minima of the curvature of the contour (K) are the points suggesting the possible presence of touching and overlaps.

Space variant threshold

• divide the image into a tessellation of squares• evaluate the Otsu threshold for each square separately

elimination of small, spurious segmented blobs identification of nuclei present in the image

Single Chromosome Measure (SCM)

• morphological dilation of the axis with a disk • evaluation of the ratio of the obtained area with that of the original blob.

Y

N

Dark pathsThe quasi-contact area along adjacent chromosomes.

OverlapsEach two of lines connecting disjoint pairs of minima points in K are considered.

Geometrical cutsCandidate cut lines links two points in K and lies entirely inside the cluster.

ClassificationClassification

The segmentation is carried out by means of a space variant thresholding scheme, which proved to be successful even in presence of hyper- or hypo-fluorescent regions in the image. Then a greedy approach is used to identify and resolve touching and overlapping chromosomes, based on geometric evidence and image information.

The classification step is coupled with a sequence of modules conceived to cope with routine images in which chromosomes are randomly rotated, possibly blurred or corrupted by overlapping or by dye stains.

Features extraction

The axis estimation is carried out by a robust modified version of a vessel-tracking algorithm.

Three features are derived from the axis: length density profile (64 samples) contour function (64 samples)

Two other geometrical features considered are: perimeter area

Axis calculation for the feature extraction

Polarization

Chromosomes are randomly rotated.

We need to comply with:

• an uniformed array feature orderliness• the orientation standard adopted

Boosted alternating decision tree:

Decision node: specify a predicate condition based on a feature.

Prediction node: specify a value to add to the polarization score.

Feature pre-processing

• different zoom • different illumination conditions• chromosomes belonging to slightly

different stages of the prometaphase

standardization needed.

Length distribution for every class,previous (up) and after (down) rescaling

Classification via Neural Network

• 3-layer ANN• 131, 131, and 24 nodes respectively.• activation functions: log-sigmoid.• training algorithm: scaled conjugate gradient

• training set: 50 karyotypes• validation set: 20 karyotypes• testing set::49 karyotypes

Class Reassigning Algorithm

The human karyotype contains 22 pairs of autosomal chromosomes and 1 pair of sex chromosomes constrained classification problem.

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.

Correctly segmented chromosomes 94%

Correctly classified chromosomes 96%

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.

Linear Programming algorithm:

rearranges the classifier outputsatisfy the above constraintsmaximize the accuracy.