The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI’s adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists’ liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.

In this work a new method to enhance and binarize document images with several kind of degradation is proposed. The method is based on the idea that by the absolute difference between a document image and its background it is possible to effectively

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.

This paper describes an original method to segment handwritten text lines from historical document images.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.