Advances in Image Segmentation: Theory and Applications

Dear Colleagues,

Segmentation is aimed at identifying the borders of objects in the analysed digital image or at splitting the image into various, non-overlapping regions. Segmentation is one of the most important steps in digital image processing systems as its result directly impacts the results of subsequent processing methods, e.g., 3D reconstruction and visualisation, distinguishing of features or classification. Practice shows that it is extremely difficult to arrive at a universal method that would produce high results for different segmentation problems that are being solved. This is all the more so as digital images are acquired with scanners/sensors of different types and with different characteristics.

Image segmentation has become a major topic of interest in various domains, including medical imaging, environmental remote sensing field, land cover applications, etc. In medical image analysis, segmentation can be defined as a method allowing, e.g., the precise shape of the potential lesions to be determined or the shape of the organ to be determined. Very different imaging techniques are available for medical use today (for example, ultrasonography (USG), computed tomography (CT), magnetic resonance imaging (MR)), while segmentation methods should be as closely matched to source images processed as possible.

In remote sensing, segmentation allows assigning labels to image pixels so that pixels in the same region or object are associated with the same label. It can be said that high spatial resolution (HSR) images acquired from planes, satellites or from unmanned aerial vehicles (UAVs) as well as from other platforms are increasingly available. HSR images come from different sensor types, such as hyperspectral, multispectral, synthetic aperture radar (SAR) or thermal infrared sensors.

This Special Issue aims to publish original papers, as well as review articles addressing emerging trends in image segmentation. The main topics include but are not limited to the following:

- Biomedical Image Segmentation (different modalities, e.g., CT, MRI, USG);

- SAR image segmentation;

- Segmentation methods for multisensor data analysis;

- Segmentation methods for time-series data analysis (e.g., agricultural crop areas, urban areas growing or drought/flood monitoring);

- Machine learning;

- Deep learning;

- Review of segmentation methods.

Dr. Marcin Ciecholewski
Dr. Cosimo Distante
Guest Editors

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