A visual model approach to extract regions of interest in microscopical images of basal cell carcinoma

Background 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]. For instance, after digitizing 1cm2 of a physical slide at a level of ×20 magnification, the resulting virtual slide amounts to a 4GB [3], that must be processed, transmitted, explored and analyzed in real time. This picture can be even worsen since a typical laboratory takes hundreds of slides each week [4]. Overall, a typical pathologist does not explore the entire slide, but instead she/he focus her/his analysis on a few number of visual fields or regions of interest (RoI ). In consequence, recognition of RoI in microscopical images may be a potential source of knowledge in many diagnostic tasks. Such RoIs would introduce new learning paradigms that would be used in medical education, medical training and diagnosis assistance. In addition, a precise determination of such regions can highly reduce the computational and transmission charge of informative regions from a sample. However, 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. This paper proposes a novel automatic approach to recognize RoIs by emulating the processing of the human visual system (HVS), not only modeling the preattentional process but also integrating it with high level processes. Hence, this paper extends our previous work [5] by including structural information about the relationships between several objects and texture recognition as higher cortex functions. These processes are necessary to minimally perceive the core of a scene, just as it is carried out within the pathologist memory [6], and therefore, to identify relevant regions for diagnosis.