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Two types of slide exploration strategy were identified for both groups: (1) a focused and efficient search, observed when the final diagnosis was correct; and (2) a more dispersed, time-consuming strategy, observed when the final diagnosis was incorrect. This difference was statistically significant, and it suggests that initial interpretation of a slide may bias further slide exploration.

This paper describes an original method to segment handwritten text lines from historical document images. After an initial preprocessing, we compute a black/white transition map to achieve a rough detection of the line regions in the image. Using this map, the corresponding line axes are extracted through a skeletonization algorithm and the conflicts between adjacent cutting lines are solved by some heuristics. Our approach was tested on a set of handwritten digitized documents (from the PROHIST Project database) from the end of the 19th century onwards. The proposed method worked well even with difficult images and it achieved an 82.18% of correct segmented lines for our database. The results of comparing our method with other recent proposal for automatic line extraction on the same test images offered more than a 38% of correct segmentation improvement.

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The Aedes Aegypti mosquito is the vector of the most difficult public health problems in tropical and semi-tropical world: the epidemic proliferation of dengue, a viral disease that can cause human beings death specially in its most dangerous form, dengue haemorrhagic fever. One of the most useful methods for mosquito detection and surveillance is the ovitraps: special traps to collect eggs of the mosquito. It is very important to count the number of Aedes Aegypti eggs present in ovitraps. This counting is usually performed in a manual, visual and non-automatic form. This work approaches the development of automatic methods to count the number of eggs in ovitraps images using image processing, particularly color segmentation and mathematical morphology-based non-linear filters.