HEP-2 CELLS CLASSIFICATION VIA FUSION OF MORPHOLOGICAL AND TEXTURAL FEATURES 13 November 2012 IEEE 12th International Conference on BioInformatics & BioEngineering I. Theodorakopoulos, D. Kastaniotis, G. Economou and S. Fotopoulos {iltheodorako, {economou, Computer Vision Group Electronics Laboratory Physics Department University of Patras, Greece

Motivation  The standard screening test for detection of autoimmune diseases is the indirect immunofluorescence (IIF) test.  Human autoantibodies (AABs) associated with various autoimmune conditions, are detected by specific fluorescence patterns on a human epithelial cell line (HEp-2).  Testing is performed manually but :  Requires highly-specialized personnel.  Time-consuming procedure.  Low standardization leads to high inter-laboratory variance.

Typical IIF Procedure Image AcquisitionImage SegmentationMitosis Detection Fluorescence Intensity Classification Staining Pattern Recognition

Typical IIF Procedure Image AcquisitionImage SegmentationMitosis Detection Fluorescence Intensity Classification Staining Pattern Recognition  Input:  Single-Cell Images  Cell Contour  Fluorescence Intensity

Taxonomy  More than thirty different nuclear and cytoplasm patterns could be identified.  Can be grouped into six basic patterns:

Properties  Different staining patterns present variations both in:  Morphological characteristics Shape complexity Holes Intensity peaks  Textural characteristics Smooth Areas Grainy Areas  In order to capture the unique properties of each pattern, incorporation of both morphological and textural descriptors seems reasonable.

Multi-level Thresholding

Morphological Features  Cell’s contour complexity  Threshold cell image into 9 levels equally spaced between intensity extremes.  Perform Connected Components Analysis on each binary image.  Discard blobs with area <1% of the mean area.  On each binary image, compute:  Number of detected blobs  Density of detected blobs  Mean solidity of the detected blobs  Concatenate all features to a 28-dimensional descriptor

Local Binary Patterns (LBPs)  A well-established textural descriptor.  The biggest part of textural information is encoded in the 58 uniform patterns.  LBPs are not rotation invariant.  A simple solution is to calculate the uniform LPBs histograms on 80 rotated instances of the cell image (4.5 deg intervals).

Local Binary Patterns (LBPs)

Classification  The 28 morphological features and the 58-bin LBPs descriptor are concatenated in a 86-Dimensional feature vector.  Classification is performed using non-linear SVMs with Gaussian kernel.

Evaluation  Lack of publicly available datasets.  Evaluation on the dataset of HEp-2 cell classification contest (hosted by ICPR 2012 conference)  721 single-cell fluorescence images.  Manually segmented and annotated by specialists in order to provide ground truth.  Binary masks and fluorescence intensity are provided.  Approximately uniform distribution of patterns.  There are not reported results for comparison yet.

Results K-fold validation - Classification performance of the various feature sets for variable k Confusion Matrix for 10-fold validation procedure using morphological and textural features’ fusion

Thank You This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund. Acknowledgment