28 February 2009 Finding regions of interest in pathological images: an attentional model approach
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603G (2009); doi: 10.1117/12.811446
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
This paper introduces an automated method for finding diagnostic regions-of-interest (RoIs) in histopathological images. This method is based on the cognitive process of visual selective attention that arises during a pathologist's image examination. Specifically, it emulates the first examination phase, which consists in a coarse search for tissue structures at a "low zoom" to separate the image into relevant regions.1 The pathologist's cognitive performance depends on inherent image visual cues - bottom-up information - and on acquired clinical medicine knowledge - top-down mechanisms -. Our pathologist's visual attention model integrates the latter two components. The selected bottom-up information includes local low level features such as intensity, color, orientation and texture information. Top-down information is related to the anatomical and pathological structures known by the expert. A coarse approximation to these structures is achieved by an oversegmentation algorithm, inspired by psychological grouping theories. The algorithm parameters are learned from an expert pathologist's segmentation. Top-down and bottom-up integration is achieved by calculating a unique index for each of the low level characteristics inside the region. Relevancy is estimated as a simple average of these indexes. Finally, a binary decision rule defines whether or not a region is interesting. The method was evaluated on a set of 49 images using a perceptually-weighted evaluation criterion, finding a quality gain of 3dB when comparing to a classical bottom-up model of attention.
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Francisco Gómez, Julio Villalón, Ricardo Gutierrez, Eduardo Romero, "Finding regions of interest in pathological images: an attentional model approach", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603G (28 February 2009); doi: 10.1117/12.811446; https://doi.org/10.1117/12.811446
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KEYWORDS
Image segmentation

Image processing

Tumor growth modeling

Visualization

Diagnostics

Tissues

Cognitive modeling

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