Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2017 - 20202

Settings

Subject: search.filters.subject.I segmentation

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A semi-automatic method for carotid artery wall segmentation in MR images
    (Institute of Electrical and Electronics Engineers Inc., 2017) Kumar, P.K.; Kesavadas, C.; Rajan, J.
    The quantification of carotid artery stenosis via imaging techniques guides the physicians to take a decision regarding surgical interventions. The measurement of wall thickness from magnetic resonance (MR) images is a promising approach to measure the degree of carotid stenosis. Manual tracing of the carotid vessel walls is time consuming and is sensitive to observer variability. Further, the existing segmentation techniques are limited by the poor contrast and presence of noise in MR images. The objective this paper is to present a novel segmentation strategy for carotid lumen and outer wall from MR images. The segmentation has been carried out in two stages which starts with a user assisted region of interest selection. In the first stage, an active contour based global segmentation has been applied to classify the lumen region. In the second stage, morphological gradient of the region of interest has been computed. This is followed by particle swarm optimization based localized segmentation to separate the wall region. The results demonstrate excellent correspondence between the automatic and manual tracings for lumen and outer walls of the carotid artery. © 2016 IEEE.
  • No Thumbnail Available
    Item
    Vocal Tract Articulatory Contour Detection in Real-Time Magnetic Resonance Images Using Spatio-Temporal Context
    (Institute of Electrical and Electronics Engineers Inc., 2020) Hebbar, S.A.; Sharma, R.; Somandepalli, K.; Toutios, A.; Narayanan, S.
    Due to its ability to visualize and measure the dynamics of vocal tract shaping during speech production, real-time magnetic resonance imaging (rtMRI) has emerged as one of the prominent research tools. The ability to track different articulators such as the tongue, lips, velum, and the pharynx is a crucial step toward automating further scientific and clinical analysis. Recently, various researchers have addressed the problem of detecting articulatory boundaries, but those are primarily limited to static-image based methods. In this work, we propose to use information from temporal dynamics together with the spatial structure to detect the articulatory boundaries in rtMRI videos. We train a convolutional LSTM network to detect and label the articulatory contours. We compare the produced contours against reference labels generated by iteratively fitting a manually created subject-specific template. We observe that the proposed method outperforms solely image-based methods, especially for the difficult-to-track articulators involved in airway constriction formation during speech. © 2020 IEEE.