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Segmentation of the subcortical structures in the brain such as the hippocampus, is known to be very challenging owing to its’ image characteristics. In brain MR images, the hippocampus is observed as a gray matter structure that often exhibits very weak or unclear boundary definitions at some fragments of its’ boundary. The unclear boundaries even cause the medical experts to misjudge the hippocampus boundary, especially at the head and tail. In this research, an automated segmentation approach, termed as Assembled and Weighted Coherent Point Drift is investigated to delineate the hippocampus accurately. Evaluations on public datasets produced an average Dice Similarity Coefficient of 0.8050, which appears better, in comparison to several other hippocampus segmentation approaches, especially against the well-known software program called Freesurfer. The study also revealed that the accuracy of the proposed segmentation approach seems on par with other various state-of-the-art approaches.

This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Glymour and Fast Incremental Association Markov Blanket proposed by Yaramakala and Margaritis), a score-based algorithm (Hill Climbing as implemented by Daly), and a hybrid algorithm (Max-Min Hill Climbing proposed by Tsamardinos et al.). Contrary to the belief regarding the superiority of constraint-based approaches, our empirical results show that a score-based approach performs better on our context dataset in terms of prediction power and learning time. The hybrid algorithm could achieve similar prediction performance as the score-based approach, but requires longer time to learn the desired network. Another interesting fact the study has revealed is the existence of strong correspondence between the linear correlation pattern within the dataset and the edges found in the learned networks. [ABSTRACT FROM AUTHOR]

A fundamental task in robotic assembly is the pick and place operation. Generally, this operation consists of three subtasks; guiding the robot to the target and positioning the manipulator in an appropriate pose, picking up the object and moving the object to a new location. In situations where the pose of the target may vary in the workspace, sensory feedback becomes indispensable to guide the robot to the object. Ideally, local image features must be clearly visible and un-occluded in multiple views of the object. In reality, this may not be always the case. Local image features are often are often rigidly constrained to a particular target and may require specialized feature localization algorithms. We present a visual positioning system that addresses feature extraction issues for a class of objects that have smooth or curved surfaces. In this work, the visual sensor consists of an arm mounted camera and a grid pattern projector that produces images with local surface description of the target. The projected pattern is always visible in the image and it is sensitive to variations in the object’s pose. A set of low-order geometric moments globally characterizes the observed pattern, eliminating the need for feature localization and overcoming the point correspondence problem. A neural network then learns the complex relationship between the robot’s pose displacements and the observed variations in the image features. After training, visual feedback guides the robot to the target from any arbitrary location in the workspace. Its applicability using a five degrees of freedom (DOF) industrial robot is demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box–whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12×10−3) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the proposed approach is compared against leading lesion segmentation algorithms using a benchmark dataset. [Copyright &y& Elsevier]