In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the Bspline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this work, the accuracy of registration results obtained using comprehensive biomechanical models is compared to the accuracy of rigid registration, the technology currently available to patients. This comparison allows us to investigate whether biomechanical modeling provides good quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 cases of neurosurgery were warped onto their respective intraoperative configurations using both biomechanics-based method and rigid registration. We used a Hausdorff distance-based evaluation process that measures the difference between images to quantify the performance of both methods of registration. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved, is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p-value<10−4). Even the modified hypothesis that less than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p-value = 0.02). The biomechanics-based method proved particularly effective for cases experiencing large craniotomy-induced brain deformations. The outcome of this analysis suggests that our nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theatre as one possible method of improving neuronavigation and surgical outcomes.

Detection of gait characteristics has found considerable interest in field of biomechanics and rehabilitation sciences. In this paper an approach for abnormal gait detection employing Discrete Fourier Transform (DFT) analysis has been presented. The joint angle characteristics in frequency domain have been analyzed and using the harmonic coefficient recognition for abnormal gait has been performed. The experimental results and analysis represent that the proposed algorithm based on DFT can not only reduce the gait data dimensionality effectively, but also lighten the computation cost, with a satisfactory distinction. In order to make the algorithm more generic, a Mean Square Error (MSE) analysis is also presented. Future work will be the expansion of the detection introduced in this system to include abnormality detection instead of just an abnormal or normal detection that would prove to be a valuable addition for use in a variety of applications, including unobtrusive clinical gait analysis, automated surveillance in addition to a variety of others.

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Biometric authentication refers to the automatic verification ofa person's identityfrom physiological or behavioral characteristics presented by him or her. In this paper an authentication scheme from hand images is presented Instead of dealing with hand measurements, typically termed as 'hand geometry', this method verifies with entire hand shape. Peg free and position invariant features are calculated using Radon Transform. Low resolution hand images captured by a document scanner are processed to extract feature vectors. The proposed scheme is tested on a data set of136 images with simple Euclidian norm based match score. The method attained an Equal Error Rate (EER) of5.1%.