Medical image segmentation is an important part of medical practice. Primarily as far as radiologists are concerned it simplifies their everyday tasks and allows them to use their time more effective, because in most cases radiologists only have a certain amount of time they can spend examining patientâ€™s data. Computer aided diagnosis is also a powerful instrument in elimination of possible human failure.
In this work, we propose a novel approach to human organs segmentation. We primarily concentrate on segmentation of human brain from MR volume. Our method is based on oversegmenting 3D volume to supervoxels using SLIC algorithm. Individual supervoxels are described by features based on intensity distribution of contained voxels and on position within the brain. Supervoxels are classified by neural networks which are trained to classify supervoxels to individual tissues. In order to give our method additional precision, we use information about the shape and inner structure of the organ. In general we propose a 6-step segmentation method based on classification.
We compared our results with those of state-of-the-art methods and we can conclude that the results are clearly comparable.
Apart from the global focus of this thesis, our goal is to apply engineering skills and best practices to implement proposed method and necessary tools in such a way that they can be easily extended and maintained in the future.
Patrik Polatsek, Miroslav Laco, Å imon DekrÃ©t, Wanda Benesova, Martina BarÃ¡nkovÃ¡, Bronislava StrnÃ¡delovÃ¡, Jana KorÃ³niovÃ¡, MÃ¡ria GablÃkovÃ¡
While psychological studies have confirmed a connection between emotional stimuli and visual attention, there is a lack of evidence, how much influence individual’s mood has on visual information processing of emotionally neutral stimuli. In contrast to prior studies, we explored if bottom-up low-level saliency could be affected by positive mood. We therefore induced positive or neutral emotions in 10 subjects using autobiographical memories during free-viewing, memorizing the image content and three visual search tasks. We explored differences in human gaze behavior between both emotions and relate their fixations with bottom-up saliency predicted by a traditional computational model. We observed that positive emotions produce a stronger saliency effect only during free exploration of valence-neutral stimuli. However, the opposite effect was observed during task-based analysis. We also found that tasks could be solved less efficiently when experiencing a positive mood and therefore, we suggest that it rather distracts users from a task.
Please cite this paper if you use the dataset:
Polatsek, P., Laco, M., DekrÃ©t, Å ., Benesova, W., BarÃ¡nkovÃ¡, M., StrnÃ¡delovÃ¡, B., KorÃ³niovÃ¡, J., & GablÃkovÃ¡, M. (2019)
Effects of individual’s emotions on saliency and visual search