Digital technology trends have changed our modern vision about storing, transmitting or visualizing histopatological specimens, under client-server architectures and regular communication networks. Emulation of an actual optical microscope, in virtual environments, is a field currently known as virtual microscopy, a new domain that has generated great expectancies about the role these technologies may play on medical diagnosis, teaching, training, research [1
] and evaluation of the pathology laboratory workflow chain [2
]. Nevertheless, the actual use of these technologies in clinical scenarios as routine tools still remains limited, among others because of the slowness of the histological digitization, the lack of standard acquisition processes, the long latency times when accessing remote computational systems and the large requirements, concerning computational resources [3
]. Development of optimal strategies to cope with these restrictions is then a priority for the field to reach a certain level of maturity. The main impact in terms of a seamless navigation is introduced by the large size of these images. In other words, these systems require huge storing spaces and dedicated communication lines, both increasing the cost of this technology. Smart compression turns out to be a pre-condition for these images to be useful but also acceleration interaction methods need to be devised. Interaction with these data could be speeded up by designing problem-related strategies such as prefetching the required data to the client side before he/she ask it. Likewise, navigation may be highly improved by storing part of this information in adapted cache spaces . Furthermore, most network and computational bottlenecks could be highly improved if Regions of Interest (RoIs) may be set beforehand. However, manual selection of RoIs in images of such size is an impossible task in clinical routine so that automation of this process results an important pre-condition for these systems to be useful, basically because this RoI setting results in probabilistic maps that may be used as initial condition of any pre-fetch or caché strategy.
A classical approach for finding RoIs in natural images has consisted in identifying regions of the image with high spatial edge density [4
]. This concept could notwithstanding hardly be applied to histopathological images because they contain regions with high edge concentration without clinical meaning [5
]. In medical images, the selection of RoIs has been approached using several methods. For instance, Karras et al. [6
], in gray scale images from abdominal cancer, hypothesizes that regions with high density of repetitive patterns were of diagnostic interest. These features were the input to a fuzzy c-means clustering algorithm that classified regions as important or non-important. This technique is not, very likely, applicable to histopathology images because information coming from color, intensity or spatial correlation [7
] results crucial for identifying diagnostic areas.
In the histopathological domain, specifically automatic cancer diagnosis, [9
], the disease was characterized at two levels: cellular, focusing on cell abnormalities, [10
] and tissular, describing changes in cell distributions [12
]. In both cases, this description was performed by low level image characterization and statistical analysis to discriminate normal from cancerous tissues. Oger et al [13
] have proposed an automated method for finding RoIs in breast tumor section. The performs an spectral analysis using a rectangular grid which represents the image as a graph, where every node corresponds to a block and every edge is weighted by a 'similarity' between the nodes(blocks) that are connected. A random walk on the data is set by the probability to pass from one node to another. The second and third eigenvectors of the transition node matrix, allow to automatically sort out the blocks by classes. Segmentation of colon glands has been achieved using graphs [14
]: a set of primitives are used to segment glands, making use of the object distribution, quantified as the definition of object-graphs.
A pathological diagnosis is the result of a complex series of activities mastered by the pathologist. Classical psycho physical theories suggest that complex visual tasks, such as histological examination, involve high degrees of visual attention [15
]. There exists evidence showing that visual systems integrate the constituting low level features of an object [16
]. These findings have inspired several computational algorithms that somehow search to capture the main meanign of the low level features [17
]. One of the most influential is the one proposed by Itti et al. [18
], a pure bottom-up attention model that locates relevant foci, based on a conjoint map of three low level characteristics, i.e., color, intensity and orientation. However, histopathology identification of diagnostic areas (regions of interest) require the association of complex visual patterns in tissues with pathologies or organs [19
], through an active search of specific features. This requirement limits the performance of Itti Model for the detection of relevat regions on histopathologic images. An automated method [20
] for finding diagnostic regions-of-interest (RoIs) in histopathological images used a modified version of the Itti’s model (Adding entropy as a key feature) to partially establish which areas could be relevant.
In this work we present a novel semi-automatic method that is able to find RoIs from histopathological images. The strategy starts by splitting the VS into an arbitrary partition of subblocks, upon which a distance to a typical relevant subblock, selected by an expert pathologist, is assigned. The metrics is defined as a non linear combination of the projection of each of these subblocks into several subspaces, each defined by different low level features. Finally, a ranking score allows to define several levels of relevancy or level sets of relevance, not necessarily connex. This article is organized as follows: next section will describe the computational method used for the extraction of regions of interest on histopathological images is also described. Section results presents the experimental evaluation, while the method performance to find RoIs is demonstrated by using the precision and recall measures. In the discusion section, we present an analysis of the results and the potential impact of them onto the virtual microscopy field.