The objective of the present work is the implementation of a Deep Learning approach using convolutional neural networks that automatically detects the presence of crackles or wheezes, anomalous respiratory sounds present during the inspiration and expiration. In addition, a proposal of a capture mechanism composed by an electret microphone and an acoustic coupler is presented. All of this, with the purpose of devising a support tool in the early respiratory disease diagnosis. Among the main conclusions, it was found that the most appropriate indicator for the model evaluation was sensitivity, where a value of 94,17% was obtained for the validation set, which shows an adequate performance. Additionally, the highest number of erroneous classifications occurred with the crackles, while the lowest in the wheezes, concluding that the system is more effective detecting the second kind of sound. In perspective, the development of a classification algorithm is proposed by taking advantage of frequential and temporal analysis, it manages to find the frequency range and respiratory cycle stage where the anomalous sound happened with the purpose of reaching more specific and precise diagnoses.
This work proposes a novel analysis of the left ventricular chamber dynamics from ultrasound 2D videos, in four steps: first the left ventricular chamber is segmented and second a multi-orientation and multi-scale filtering is performed. A third step is the chamber partition in similar number of super-pixels or homogeneous regions. The final step extracts features from the velocity-acceleration phase plane constructed by tracking these regions along the cardiac cycle and estimating their velocity and acceleration. Finally, each case is characterized by dividing the phase plane into three disjoint areas along the radial direction and estimating the density of points per region. This approach was evaluated in actual videos of four subjects, two control and two patients. Results show density means for unhealthy and control as follows: 0.63 and 0.45 for the low motion region, 0.26 and 0.4 for the mid motion region, and 0.1 and 0.1 for the high motion region.
Speckle noise filtering has been investigated since at least fifty years, this multiplicative and granular interference may be found in any image, i.e, Synthetic Aperture Radar (SAR), optical coherence tomography, and of course, medical ultrasound imaging. Speckle noise is produced by structural characteristics of materials, in case of the ultrasound imaging case, by small structural irregularities. This work proposes a novel speckle noise filtering strategy using a bank of morphological multi-scale filters that captures anisotropic information and additionally preserves cardiac structures. This method is compared against commonly used filters, namely: Anisotropic Diffusion Filter (ADMSS), Non-Local Means Filter (NLMF) and Detail Preserving Anisotropic Diffusion Filter (DPAD).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.