In this WP, the algorithms developed in WP 1&2 will be combined and optimized
for FL image classification and rating. Dr. Gurcan’s group at OSU has already
established databases for the development of algorithms, secured institutional
review board approvals, created the software infrastructure to classify and rate
FL images using supercomputers, which involve the following image processing
- Follicle region detection in IHC images
- registration of IHC images to H&E images
- follicle region detection in H&E images using the registration outcome
- detection of centroblasts and non-centroblasts, and integration and
optimization of the overall system.
IHC images have better contrasts between follicle and inter-follicle regions as
compared to H&E images; that is why the whole process starts with IHC images.
The detected follicle regions in IHC images are then mapped to H&E images where
follicle boundaries are further adjusted. Since this is a complex and
multi-dimensional problem, it requires close cooperation of image recognition
and pattern recognition experts. All the European partners have already attacked
similar medical and non-medical problems and have several years of expertise in
WARWICK has developed a wavelet transform-domain method, which adaptively
selects the best wavelet features that are optimal in discriminating between
different classes of histology images. The covariance and co-difference matrices
constructed from optimally selected wavelet features and MRF segmentation
algorithms, will be investigated in both IHC and H&E images for follicular
Similarly, the Quantitative Histo-Imagery team of the GRECAN has been working on
segmenting and extracting features from low resolution virtual slides of whole
tumor tissue section since 1995. GRECAN recently started using a multi-scale
wavelet approach, the team adapts various tools  developed for low
resolution images in order to process high resolution virtual slides. The first
step of the process is subsampling of high-resolution virtual slides acquired at
a resolution of 0.25µm, 0.5µm or more (up to the resolution of 4µm). In order to
preserve structural details and colours during the subsampling process, an
algorithm based on wavelet filtering is used. This allows processing the whole
slide at once on a standard PC, in order to detect and localize
objects-of-interest at low-resolution. Computation of some features can also be
done at this stage, in order to perform a pre-classification of tumors. After
that, the boundaries of objects-of-interest can be mapped to the high-resolution
virtual slides for visual control. The objects can be copied and pasted into a
blank image series to generate a high-resolution gallery. These galleries will
then be used for refining segmentation results and extracting high-resolution
The joint expertise in multi-scale image analysis and medical image processing
will bring computationally efficient solutions to the subject problem and will
contribute to Ph.D. theses of young scientists in the MIRACLE group.
image classification and rating method for FL images.
3.1: Refinement of the methods developed in WP 1 and WP 2.
3.2: Identification of follicle regions in segmented FL images.
Identification of centroblast and non-centroblast regions.
3.4: Automatic interpretation of FL images.
12) Report on current methods of FL image analysis.
13) Algorithm for identification of follicle regions in segmented FL images.
14) Algorithm for identification of centroblast and non-centroblast regions.
15) Automatic interpretation and rating software.
16) Final report on the scientific work carried out in WP3 during the
Metin N. Gurcan, Olcay Sertel, Jeffrey Prescott
Prof. A. Enis Cetin, Alexander Suhre, Ibrahim Onaran
-WARWICK: Dr. Nasir Rajpoot, Researcher 1, Researcher 2
Dr. Paulette Herlin, Researcher 5