Ehsan Arbabi

Fast collision detection methods for joint surfaces

In the recent years medical diagnosis and surgery planning often require the precise evaluation of joint movements. This has led to exploit reconstructed three-dimensional models of the joint tissues obtained from CT or MR Images (for bones, cartilages, etc.). In such context, efficiently and precisely detecting collisions among the virtual tissues is critical for guaranteeing the quality of any further analysis. The common methods of collision detection are usually designed for general purpose applications in computer graphics or CAD-CAM. Hence they face worst case scenarios when handling the quasi-perfect concavity-convexity matching of the articular surfaces. In this paper, we present two fast collision detection methods that take advantage of the relative proximity and the nature of the movement to discard unnecessary calculations. The proposed approaches also accurately provide the penetration depths along two functional directions, without any approximation. They are compared with other collision detection methods and tested in different biomedical scenarios related to the human hip joint.

Penetration depth method—novel real‐time strategy for evaluating femoroacetabular impingement

We introduce a new method for computerized real‐time evaluation of femoroacetabular impingement (FAI). In contrast to previously presented stress analyses, this method is based on two types of predictions of penetration depths for two rotating bodies: curvilinear and radial penetration depth. This intuitive method allows the analysis of both bony and soft tissue structures (such as cartilage and acetabular labrum) in real time. Characteristic penetration depth patterns were found for different subtypes of FAI, such as cam and pincer pathologies. In addition, correlation between the penetration depths (estimated by applying this method) and the existing contact stresses (estimated by applying the finite element method) of various hip morphologies were found. A strong correlation with predicted stress values existed, with a mean correlation coefficient of 0.91 for the curvilinear and 0.80 for the radial penetration method. The results show that the penetration depth method is a promising, fast, and accurate method for quantification and diagnosis of FAI.

A Fast Method for Finding Range of Motion in the Human Joints

Finding the range of motion for the human joints is a popular method for diagnosing joint diseases. By current technology, it is more trustable and easier to find the range of motion by employing computer based models of the human tissues. In this paper we propose a novel method for finding range of motion for human joints without using any collision detection algorithm. This method is based on mesh classifying in a cylindrically segmented space. The method shows to be much faster than the traditional ones and provides the accurate results. This method is illustrated on the case of finding the range of motion in the human hip joint.

Sensitivity of hip tissues contact evaluation to the methods used for estimating the hip joint center of rotation

Computer-based simulations of human hip joints generally include investigating contacts happening among soft or hard tissues during hip movement. In many cases, hip movement is approximated as rotation about an estimated hip center. In this paper, we investigate the effect of different methods used for estimating hip joint center of rotation on the results acquired from hip simulation. For this reason, we use three dimensional models of hip tissues reconstructed from MRI datasets of 10 subjects, and estimate their center of rotation by applying five different methods (including both predictive and functional approaches). Then, we calculate the amount of angular and radial penetrations that happen among three dimensional meshes of cartilages, labrum, and femur bone, when hip is rotating about different estimated centers of rotation. The results indicate that hip simulation can be highly affected by the method used for estimating hip center of rotation. However, under some conditions (e.g. when Adduction or External Rotation are considered) we can expect to have a more robust simulation. In addition, it was observed that applying some methods (e.g. the predictive approach based on acetabulum) may result in less robust simulation, comparing to the other methods.

Human identification with EEG signals in different emotional states

This paper investigates human identification using EEG signals. It has been shown that Electroencephalogram (EEG) can be used as a trait for biometric systems. Previous studies have reported proper channels and features in resting states and mental tasks. However, since EEG signal is sensitive to emotion, the stability of reported features during emotional states is not well verified. Our goal is to investigate channels and features which have stable results regardless of emotional states. To this end, three experiments were designed: ‘training’ and ‘testing’ an identification system with 1) mixture of emotional states; 2) the same specific emotional states; 3) different emotional states. 1728 features were extracted which later construct the feature vector of each subject and then Support Vector Machine (SVM) was used to classify the subjects. After selecting 5 best features, the Correct Classification Rate (CCR) is in the range of 88% to 99% for 3 experiments. Moreover, we found that features extracted from Gamma frequency band in Left-Posterior quarter of the brain have more stable and reliable information for human identification, regardless of emotional states, comparing to other features.

Weighted Vote Fusion in prototype random subspace for thermal to visible face recognition

The human body, like all other objects with temperature above the absolute zero, emits electromagnetic wave. The emission of infrared electromagnetic wave from the human face produces thermal images. Thus thermal images can be formed even in dark conditions, in which the formation of the visible image is impossible. However, the majority of the stored images in the recognition systems are visible. Thus, matching the thermal probe and visible gallery images can solve the night time face recognition problem. On the other hand, because of the different formation mechanism of these two types of images, there are lots of challenges in the matching process. Prototype random subspace approach is one of the most successful methods in the area of thermal to visible face recognition. In this paper, we have revised the recognition step of prototype random subspace approach by proposing Weighted Vote Fusion scheme. The proposed strategy has been tested on an available data set and the results show about 9% of improvement in recognition rate, comparing to the original approach.

Anatomy-based 3D skeleton extraction from femur model

Abstract Using 3D models of bones can highly improve accuracy and reliability of orthopaedic evaluation. However, it may impose excessive computational load. This article proposes a fully automatic method for extracting a compact model of the femur from its 3D model. The proposed method works by extracting a 3D skeleton based on the clinical parameters of the femur. Therefore, in addition to summarizing a 3D model of the bone, the extracted skeleton would preserve important clinical and anatomical information. The proposed method has been applied on 3D models of 10 femurs and the results have been evaluated for different resolutions of data.

A low cost hands-free typing system for disabled people

Virtual reality can provide the means of computer interaction for disabled people. There are many methods for aiding disabled people to communicate with computer; however some of them may require high amount of processing and cannot be implemented in real-time applications. In addition, many methods may require additional equipment to be attached to the users, which can reduce freedom of movement and increase cost of the system. In this article, we propose a real time, low cost and hands free method for typing; only utilizing users head movements. Head movement detection is done by a color-marker discrimination algorithm, which compares differences between RGB, hue and saturation components of the markers and the environment. The method has shown to be adoptable to different lighting conditions. Also, trained users showed to be able to type a text with a reasonable speed (4.5 s for each character), and with a low error rate (0.0% to 5.5%).

A novel automated three-dimensional framework for evaluating alpha angle in femur

Femoroacetabular Impingement (FAI) is considered as one of the main causes of early osteoarthritis of the hip. Cam impingement is femoral cause of FAI, which refers to a situation where the femoral head loses its spherical shape at head-neck junction. This anatomic deformity could be reflected by the alpha angle. In this article we propose a new fully automated framework for measuring alpha angle using 3D data, without any need of manual configuration. This approach provides alpha angles in different orientations, by rotating a radial plane around the femoral head-neck axis. This helps to have an illustration of alpha angle as a continuous curve. The method has been tested on 12 femur 3D datasets (6 left + 6 right), reconstructed from MR images of different female subjects without any reported femoral abnormalities. The mean and standard deviation values of alpha angle are about 36.9° and 1.1° respectively, which are in good agreement with the expected values for alpha angle. In addition, four different resolutions of 3D meshes have been examined by the proposed method. The effect of the resolution on the provided results has been evaluated (in terms of accuracy and speed).

Estimating Hip Joint Contact Pressure from Geometric Features

Hip mechanical simulation for estimating pressures within the soft tissues during the loads and motions of daily activities is a common approach to investigate hip joint pathology. Many computational approaches estimate the pressure and contact pressures via finite element methods (FEM) by using 3D meshes of the tissues. Although this type of simulation can provide a good evaluation of hip problems, the process may be very time consuming and unsuitable for fast medical hip simulations. In this chapter, a statistical model is proposed for estimating hip pressures during its movement, by using the geometrical features extracted from 3D meshes of different hip models. The method is tested by examining 25 different hip models during a frequent daily activity.