Healthcare systems involve hospitals, clinics, and community agencies which are peculiar with respect to other work environments. The automation process can improve the services by providing the healthcare systems with algorithms for image processing. In this regard, segmentation algorithms play a fundamental role. Image segmentation consists in partitioning any kind of digital image (e.g., medical, astronomical, and natural) into multiple sets of pixels. The aim of the segmentation process consists in simplifying and/or transforming the image representation so that it will be easier to analyze. A typical application of image segmentation is the localization of objects and boundaries in digital images. In particular, the final result of the image segmentation process is a new image where a label is assigned to every group of pixels: all the pixels with the same label have the same feature in common. Each group of pixels represents a region of the original image associated with an object to recognize (such as an organ in a CT image, or a lesion in a MR image). In general, the pixels belonging to a region share the same feature, such as an intensity or color, or a computed feature such as texture. Some examples of applications based on image segmentation include multispectral images for clinical monitoring, tools based on image acquisition requiring postprocessing, mobile devices for image acquisition through cameras or other types of scanners, assistance applications oriented to disabled people, collaborative virtual environments for physicians including image analysis tasks, and so on.

Image segmentation can be integrated into many applications regarding healthcare systems, such as devices using a particular image sensor (e.g., a thermal camera) with built-in segmentation software or a device equipped with a normal camera (e.g., a smartphone) can be used as diagnostic devices for cutaneous condition or oral medicine. Moreover, image segmentation can support telemedicine software for the elderly care or in the domestic environment of frail patients, to perform the segmentation of medical images with the aim to highlight lesions or other pathologies.

All researchers are invited to submit papers regarding their last advances in image segmentation for automatic or semiautomatic medical methodologies.

Potential topics include but are not limited to the following:

• Remote support for medical diagnosis using image segmentation
• Image segmentation for healthcare devices
• Clinical monitoring and management using image segmentation techniques
• Clinical equipment, including software and hardware
• Advances in medical imaging, including segmentation/interpretation
• Mobile applications and low cost systems
• Telemedicine systems for elderly care
• Diagnosis support systems