A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

Adaptive Graph Construction for Isomap Manifold Learning

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ1 norm. The ℓ1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap

EXTRACTION OF FETAL HEART ACTIVITY BY USING BISPECTRUM-BASED ELECTROMYOGRAPHY SIGNAL PROCESSING

This paper proposes the use of third-order spectrum named bispectrum that can be applied to biomedical diagnostics as a useful tool for detection of fetal heart activity. Fetal heart activity detection, recognition and analysis are proposed by extraction of informative features contained in bispectrum estimates computed for the electromyography signals measured on the abdominal surface of pregnant woman. Experimental investigations have been performed by using the recorded and accumulated abdominal electromyography signals. Contributions extracted in bispectral domain by computation of bimagnitude estimates demonstrate detection of novel class of information features indicating the phase coupling relationships between the frequencies contained in the abdominal electromyography signals. This phase coupling content can serve as discriminative feature for extraction fetal heart activity contribution in the intricate interference environment. Obtained results demonstrate effective interference suppression provided by using bispectrum-based signal processing.acceptedVersionPeer reviewe

RECOGNITION OF PREMATURE BIRTHS BY BISPECTRUM-BASED ABDOMINAL ELECTROMYOGRAPHY SIGNAL PROCESSING

A novel technique for detection and recognition of normal and premature births is proposed and experimentally examined. It is based on the extraction of novel class of informative features contained in higher-order spectrum, namely, bispectrum of the abdominal electromyography signals registered on the abdominal surface of pregnant woman. It is demonstrated that the amplitude bispectrum, phase bispectrum and bicoherence signatures computed for electromyography signals can serve as the perspective facilities for detection and recognition of the normal and premature births. The proposed bispectrum-based information features were studied by real-life experimental data processing. Uterine activity corresponding to two weeks and one week before birth for several patients has been investigated. Experimental results obtained for a number of the patients demonstrate the possibility to extract novel classification features contained in the computed biamplitude, biphase and bicoherence signatures.acceptedVersionPeer reviewe