Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

We present an automatic technique for image alignment using principal component analysis (PCA) that broadly consists of two steps. The first step is the segmentation of the region of interest by thresholding. In the second step, PCA is applied on nonzero pixels in the binary image to determine the object rotation about the mean of the object pixels. Existing PCA-based techniques align the data in their principal spread; however, they have a critical problem of 180° rotation in their principal axes. The current paper provides an automatic solution to address this problem. The algorithm is based on assignment rules inferred from the eigenvectors given by PCA. We applied the proposed algorithm to different datasets including handwritten images of digits, a rotated fingerprint image dataset, and a dataset of magnetic resonance brain images, and confirmed that the proposed method aligns the data efficiently and accurately. In addition to alignment, the algorithm proposes two standard orientations for automatically assessing the true side (upside) of an object.

An anthropomorphic prosthetic hand for wrist or forearm amputees is developed herein. The prosthetic hand was designed with an underactuated mechanism, which makes self-adaptive grasping possible, as well as natural motions such as flexion and extension. The finger and thumb modules were designed with four degrees of freedom by motions of the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints. In this research, we pursued several novel trials in prosthetic hand design. By using two four-bar linkages composed of a combination of linkages and gears for coupling joints at each finger, it was possible to make a compact design, and the linkage has advantages such as accurate positioning, uniform power transmission, and high payload. Also, by using constant-velocity joints, torque is transferred to finger modules regardless of adduction/abduction motions. In addition, adduction/abduction and self-adaptive grasping motions are passively realized using torsional springs. The developed prosthetic hand was fabricated with a weight of 475 g and a human hand size of 175 mm. Experiments with diverse objects showed its good functionality.

Head pose estimation (HPE) has been extensively investigated over the past decade due to its wide range of applications across several domains of artificial intelligence (AI), resulting in progressive improvements in accuracy. The problem becomes more challenging when the application requires full-range angles, particularly in unconstrained environments, making HPE an active research topic. This paper presents a comprehensive survey of recent AI-based HPE tasks in digital images. We also propose a novel taxonomy based on the main steps to implement each method, broadly dividing these steps into eleven categories under four groups. Moreover, we provide the pros and cons of ten categories of the overall system. Finally, this survey sheds some light on the public datasets, available codes, and future research directions, aiding readers and aspiring researchers in identifying robust methods that exhibit a strong baseline within the subcategory for further exploration in this fascinating area. The review compared and analyzed 113 articles published between 2018 and 2024, distributing 70.5% deep learning, 24.1% machine learning, and 5.4% hybrid approaches. Furthermore, it included 101 articles related to datasets, definitions, and other elements for AI-based HPE systems published over the last two decades. To the best of our knowledge, this is the first paper that aims to survey HPE strategies based on artificial intelligence, with detailed explanations of the main steps to implement each method. A regularly updated project page is provided: (github).