Recurrent nerve paralysis (RP) is one of the most frequent complications of thyroid surgery. It reduces vocal fold mobility. Nasal endoscopy, a mini-invasive procedure, is the conventional way to detect RP. We suggest a new approach based on laryngeal ultrasound and a specific data analysis was designed to help with the automated detection of RP. Ten subjects were enrolled for this feasibility study: four controls, three patients with RP and three patients without RP according to nasal endoscopy. The ultrasound protocol was based on a ten seconds B-mode acquisition in a coronal plane during normal breathing. Image processing included three steps: 1) automated detection of two consecutive closing and opening images, corresponding to extreme positions of vocal folds in the sequence of B-mode images, using principal component analysis of the image sequence; 2) positioning of three landmarks and robust tracking of these points using a multi-pyramidal refined optical flow approach; 3) estimation of quantitative parameters indicating left and right fractions of mobility, and motion symmetry. Results provided by automated image processing were compared to those obtained by an expert. Detection of extreme images was accurate; tracking of landmarks was reliable in 80% of cases. Motion symmetry indices showed similar values for controls and patients without RP. Fraction of mobility was reduced in cases of RP. Thus, our CAD system helped in the detection of RP. Laryngeal ultrasound combined with appropriate image processing helped in the diagnosis of recurrent nerve paralysis and could be proposed as a first–line method.
An automated method based on maximizing a 2D correlation coefficient between images is proposed to realign consecutive images obtained in functional MR image sequences. Dynamic Gadolinium-enhanced osteosarcoma studies are analyzed to study the influence of interframe motion and assess the registration method. The alignment procedure is evaluated by subtraction of the pre-injection image and by factor analysis of medical image sequences. The effect of motion correction is demonstrated by both techniques and the correlation method is compared to a procedure based on external markers.
We present a new clustering algorithm for medical images sequences (CAMIS). It combines criteria of spatial contiguity, signal evolution similarity, and the rule of mutual nearest neighbors. The statistical properties of the signal in the images (CT, MRI, nuclear medicine) is taken into account when choosing the dissimilarity index and is explicitly expressed for scintigraphic images. The partition, into an unknown number of classes, was updated by merging and pruning clusters. The efficiency of CAMIS as the first step of factor analysis of medical image sequences has been tested using simulated scintigraphic images.
KEYWORDS: Statistical analysis, Principal component analysis, Medical imaging, Image processing, Data modeling, Magnetic resonance imaging, Factor analysis, Statistical modeling, Signal to noise ratio, Nuclear medicine
A new statistical approach for Factor Analysis of Medical Image Sequences (FAMIS) is proposed. It leads to the optimal metric to be used in the orthogonal and oblique analysis steps of FAMIS. It is shown that this metric depends on the statistical model related to the image acquisition process and we derived its expression for nuclear medicine and magnetic resonance imaging. A scintigraphic dynamic study illustrates the method. We discuss the normalization induced by this optimal metric in comparison with other normalizations.
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