New methods on image analysis: augmented reality application for heartbeat sound analysis and MRI brain injury images

Abstract: Traditional statistical methods have become insufficient when applied to image analysis. The increasing size of data volume and its complexity demands new statistical approaches and algorithms. Current methods imply losing intrinsic data structures, for example when data comes from multiway arrays. In this work we concentrate in two applications i) A pre-diagnostic smartphone application for detection of cardiovascular abnormalities through the analysis of heartbeat sounds and the use of augmented reality for displaying valuable information to the end user in an immersive experience. Using the latest augmented reality smartphone applications, a digital stethoscope, heartbeat audios and classification using neural networks, we measure a user’s heartbeat and output in real time, a pre-diagnostic of their current cardiovascular health. ii) A study concerning comatose patients, based on a Diffusion Tensor Image Magnetic Resonance Imaging (MRI) dataset that predicts long-term outcome for patients having suffered a brain traumatic injury. MRI images were obtained from 104 comatose patients, 65 with positive outcome and 39 with negative outcome, 39 controls were used. The fact that each volumetric image led into a 143x255726x4 tensor input, is used to briefly explain how new multiway methods could be useful in image analysis methods.

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