Summary
Objectives:
This paper aims at introducing a novel approach for segmentation of overlapping objects
and at demonstrating its applicability to medical images.
Methods:
This work details a novel approach enhancing the known theory of full-segmentation
of an image into regions by lifting it to a semantic segmentation into objects. Our
theory allows the formal description of partitioning an image into regions on the
first level and allowing the occurrence of overlaps and occlusions of objects on a
second, semantic level. Possible applications for the use of this ‘semantical segmentation‘
are the analysis of radiographs and micrographs. We demonstrate our approach by the
example of segmentation and separation of overlapping cervical cells and cell clusters
on a set of 787 image pairs of registered PAP- and DAPI-stained micrographs. The semantical
cell segmentation yielding areas of cell plasmas and nuclei are compared to a manual
segmentation of the same images, where 2212 cells have been labeled. A direct comparison
of over and under-segmentation between the two segmentation sets yields a mean difference
value of 10.15% for the nuclei and 10.80% for the plasma.
Results:
Using the proposed theory of semantical segmentation of images in combination with
adequate models of the image contents, our approach allows identifying, separating
and distinguishing several overlapping, occluding objects in medical images. Applying
the proposed theory to the application of cervical cell segmentation from overlapping
cell clusters and aggregates, it can be seen that it is possible to formally describe
the complex image contents.
Conclusions:
The proposed method of semantical segmentation is a mighty tool and under the assumption
of the subtractive transparency model can be used in different medical image processing
applications such as radiology and microscopy. By using alternative models to solve
the ambiguities attached to overlaps and occlusions, further fields of application
can be addressed.
Keywords
Algorithms - computer-assisted image processing - computer-assisted image interpretation
- photomicrography - fluorescence microscopy - image segmentation - overlapping objects
- occluding objects
