Follicle regions in both IHC and H&E stained microscopic images have to be
determined in an automatic manner. In this WP, computationally efficient image
segmentation methods will be investigated. Furthermore, microscopic images are
large in size and some parts of the image do not contain any clinically
significant information, therefore they have to be segmented into subregions
before further processing to reduce the overall computation cost. For this
reason, we will study covariance based and MRF based image segmentation methods.
Recently, Donoser et al.  developed a covariance matrix based image
segmentation method for regular images. Covariance and co-difference matrix
based segmentation method for microscopic images will be also developed in this
WP. As described in WP1, the co-difference method will be computationally more
efficient than the covariance method, because the co-difference matrix can be
computed without performing any multiplications.
Another probabilistic segmentation approach is based on Markov Random Fields (MRF)
, which model image properties as a random field whose probability density
function is assumed to be Gibbsian. Graph cut techniques can be effectively used
to find the global minimum of the energy function representing the smoothness
[21,22] and consistency of the image data in MRFs and they are fast enough to be
practical . These techniques consider a weighted graph for the energy
function with two terminal vertices called the source and sink. A cut is a set
of edges such that the terminals are separated in two disjoint sets. In weighted
graphs, the cost of the cut is defined to be the sum of weights of the edges
crossing the cut. The minimum cut problem is to find the cut with the minimum
sum. The basic idea is that the minimum cut minimizes the energy either globally
or locally. The minimum cut, in turn, can be computed very efficiently by
In the specific problem of Follicle Lymphoma Classification, five output classes
are defined (nuclei, cytoplasm, extracellular material, background and red blood
Graph cuts can quite easily incorporate other information or smoothness
constraints, e.g. edges, detected features e.g. to define better energy
functions to be minimized, so that improved segmentation results can be
obtained. More specifically, parameters, such as color, intensity wavelet
coefficients and wavelet packets will be used.
In addition, the use of Graph-cut techniques to higher dimensionality images
obtained by registrating IHC and H&E images will be studied. This may
significantly improve the segmentation result. A registration module for these
images is already available at OSU, however the consortium intends to perform
additional research in this field ((e.g. based on SIFT feature extraction and
matching ). In this WP, all of the methods will be compared with each other
and the currently available methods. Final result will be a computationally
efficient segmentation algorithm, which will not require supercomputers for
segmenting microscopic images in the future.
Segmentation and Model-based Representations
segmentation algorithms for IHC and H&E images of follicular lymphoma using
covariance, co-difference and MRF based methods
2.1: Automatic segmentation of IHC images.
2.2: Automatic segmentation of H&E images.
Registration of IHC and H&E images.
Testing in the OSU FL image database.
6) Report on
segmentation methods for FL images.
7) MRF based
based segmentation algorithm.
Co-difference based segmentation algorithm.
Registration of IHC and H&E images algorithm.
report on the scientific work carried out in WP2 during the exchange
Dr. Nikolaos Grammalidis, Dr. Sotitis Malassiotis, Dr. Kosmas Dimitropoulos,
Researcher 3, Researcher 4
Metin N. Gurcan, Jeffrey Prescott
Prof. A. Enis Cetin, Alexander Suhre, Kivanc Kose