Abstract
During the exploration of a microscopical slide the pathologist focuses the analyses on a few number of regions of interest. An accurately recognition of those regions may be a potential source of knowledge in many diagnostic task. However, due to the intrinsic complexity of the histopathological images, it is not a straightforward task. The method herein proposed is based on the cognitive process of looking for structures of interest.
Highlights
The virtual microscopy is a discipline that emulates the interaction between an expert with a microscopical sample upon a high resolution digital slide [1]
Such regions of interest (RoI) would introduce new learning paradigms that would be used in medical education, medical training and diagnosis assistance
Once the algorithm sets up the first carcinoma region within the image, the other carcninoma regions are defined as the most similar regions using an Euclidean metrics of the different basic features: color, entropy and image intensity
Summary
The virtual microscopy is a discipline that emulates the interaction between an expert with a microscopical sample upon a high resolution digital slide [1] This type of technology is used for medical training and medical education, but so far it has been exclusively used in research environments because of the large computational requirements [2]. Automatic recognition of such regions is really a challenging task because of the inherent randomness of tissue’s cutting, color tissue properties and tissue orientation. In spite of these difficulties, the pathologist efficiently recognizes regions of interest in several domains by fusing image and task dependent information into a unique framework.
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