Infectious causes of Alice in Wonderland syndrome.

Alice-in-Wonderland syndrome (AIWS) is a perceptual disorder embracing a spectrum of self-experienced paroxysmal body image illusions including most commonly distortions of shape (metamorphopsia), size (macropsia or micropsia), distance (pelopsia or teleopsia), movement, and color among other visual and somesthetic distortions. Depersonalization, derealization, and auditory hallucinations have also been described. Recent reports suggest that infectious diseases are the predominant etiology for AIWS, especially among children. This article reviews current understanding regarding the association between infection and development of AIWS.

Alice in Wonderland syndrome: a novel neurological presentation of Zika virus infection.

Zika virus (ZIKV) is a flavivirus endemic in Africa and Southern Asian countries, which has recently emerged in unprecedented epidemic proportions around the world. Although ZIKV infection is often asymptomatic or distinguished by non-specific influenza-like symptoms, an increase in its pathogenicity and biological behavior has been the hallmark of the current pandemic. Increasing evidence suggests that neurotropic strains of ZIKV have evolved from less pathogenic strains of the virus. Neurological manifestations of ZIKV infection include a spectrum of congenital and non-congenital clinical entities, however visual somatosensory perceptual disorders have not been recorded to date. Herein, we report a case of a 15-year-old female who presented with a constellation of perceptual symptoms (metamorphopsia, telopsia, and pelopsia) following acute ZIKV infection. Although such symptoms may have originated from direct viral injury, a post-ZIKV autoimmune reaction to previously unexposed neuronal surface antigens or through molecular mimicry cannot be excluded. The development of Alice in Wonderland syndrome in our patient highlights the ever-increasing expanding spectrum of neurological symptoms associated to ZIKV infection.

A new strategy to obtain robust markers for blood vessels segmentation by using the watersheds method.

The watersheds method is a powerful segmentation tool developed in mathematical morphology. In order to prevent its over-segmentation, in this paper, we present a new strategy to obtain robust markers for segmentation of blood vessels from malignant tumors. For this purpose, we introduced a new algorithm. We propose a two-stage segmentation strategy which involves: (1) extracting an approximate region containing the blood vessel and part of the background near the blood vessel, and (2) segmenting the blood vessel from the background within this region. The approach effectively reduces the influence of peripheral background intensities on the extraction of a blood vessel region. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed strategy was tested on manual segmentation, where segmentation errors less than 10% for false positives and 0% for false negatives are observed. It is demonstrated by extensive experimentation, by using real images, that the proposed strategy was suitable for our application in the environment of a personal computer.