Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

A novel and stable approach to anatomical structure morphing for enhanced intraoperative 3D visualization

Abstract: The use of three dimensional models in planning and navigating computer assisted surgeries is now well established. These models provide intuitive visualization to the surgeons contributing to significantly better surgical outcomes. Models obtained from specifically acquired CT scans have the disadvantage that they induce high radiation dose to the patient. In this paper we propose a novel and stable method to construct a patient-specific model that provides an appropriate intra-operative 3D visualization without the need for a pre or intra-operative imaging. Patient specific data consists of digitized landmarks and surface points that are obtained intra-operatively. The 3D model is reconstructed by fitting a statistical deformable model to the minimal sparse digitized data. The statistical model is constructed using Principal Component Analysis from training objects. Our morphing scheme efficiently and accurately computes a Mahalanobis distance weighted least square fit of the deformable model to the 3D data model by solving a linear equation system. Relaxing the Mahalanobis distance term as additional points are incorporated enables our method to handle small and large sets of digitized points efficiently. Our novel incorporation of M-estimator based weighting of the digitized points enables us to effectively reject outliers and compute stable models. Normalization of the input model data and the digitized points makes our method size invariant and hence applicable directly to any anatomical shape. The method also allows incorporation of non-spatial data such as patient height and weight. The predominant applications are hip and knee surgeries.

Multi-class SVM based iris recognition

Abstract: We propose an improved iris recognition method to identify the person accurately by using a novel iris segmentation scheme based on the chain code and the collarette area localization. The collarette area is isolated as a personal identification pattern, which captures only the most important areas of iris complex structures, and a better recognition accuracy is achieved. The idea to use the collarette area is that it is less sensitive to the pupil dilation and usually not affected by the eyelids or the eyelashes. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique and used to train the support vector machine (SVM) as iris pattern classifiers. The parameters of SVM are tuned to improve the overall system performance. Our experimental results also indicate that the performance of SVM as a classifier is far better than the performance of backpropagation neural network (BPNN), K-nearest neighbor (KNN), Hamming distance and Mahalanobis distances. The proposed innovative technique is computationally effective as well as reliable in term of recognition rate of 99.56%.