Visual search in ADHD, ASD and ASD + ADHD: overlapping or dissociating disorders?

Recent debates in the literature discuss commonalities between Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) at multiple levels of putative causal networks. This debate requires systematic comparisons between these disorders that have been studied in isolation in the past, employing potential markers of each disorder to be investigated in tandem. The present study, choose superior local processing, typical to ASD, and increased Intra-Subject Variability (ISV), typical to ADHD, for a head-to-head comparison of the two disorders, while also considering the comorbid cases. It directly examined groups of participants aged 10-13 years with ADHD, ASD with (ASD+) or without (ASD-) comorbid ADHD and a typically developing (TD) group (total N = 85). A visual search task consisting of an array of paired words was designed. The participants needed to find the specific pair of words, where the first word in the pair was the cue word. This visual search task was selected to compare these groups on overall search performance and trial-to-trial variability of search performance (i.e., ISV). Additionally, scanpath analysis was also carried out using Recurrence Quantification Analysis (RQA) and the Multi-Match Model. Results show that only the ASD- group exhibited superior search performance; whereas, only the groups with ADHD symptoms showed increased ISV. These findings point towards a double dissociation between ASD and ADHD, and argue against an overlap between ASD and ADHD.

Computer aided detection of microcalcifications in digital mammograms.

Microcalcification detection is widely used for early diagnosis of breast cancer. Nevertheless, mammogram visual analysis is a complex task for expert radiologists. In this paper, we present a new method for computer aided detection of microcalcifications in digital mammograms. The detection is performed on the wavelet transformed image. The calcifications are separated from the background by exploiting the evaluation of Renyi's information at the different decomposition levels of the wavelet transform. Experiments are performed on a standard and publicly available dataset and the results are evaluated with respect to recent achievements reported in the literature.

Coupling the world with the observer: from analysis of information to active vision.

In this paper we define the content of information in an image and show how it can be computed by taking into account different levels of resolution, in the framework of information theory and the thermodynamics of irreversible transformations. The results thus obtained will eventually be exploited to derive a mechanism for active exploration of visual space suitable to perform a dynamic coupling between the agent and its environment.

An information-theoretic approach to interactions in images.

In this paper it will be argued that the notion of interactions in images is closely related to that of entropy associated with an image, and it will be shown that interactions make processing of the information coming from the retina computationally less expensive. A procedure will be presented, based on the evolution of joint entropy across different scales, to gauge the contributions of different types of interactions to the structure of the images.

Multiresolution spot detection by means of entropy thresholding.

Many imaging applications deal with the detection of small targets or spots embedded within an inhomogeneous background. We present a method that accomplishes a multiresolution detection on the wavelet-transformed image. The targets are separated from the background by the exploitation of Renyi's information, which is evaluated at the different decomposition levels of the wavelet transform. The scale-dependent candidate detections are successively combined by means of majority voting for final detection. Connections with results provided in different fields such as multifractal analysis, generalized information measures in scale-space, and cross-entropy analysis in fine-to-coarse transformations are discussed. Detection performance is investigated through an example from medical image analysis.