Pascal Makris

Origin of the Sound Components During Pharyngeal Swallowing in Normal Subjects

The aim of this study was to identify the origin of swallowing sound components by using modern techniques that can provide numeric, synchronized acoustic–radiologic data. We enrolled 15 volunteer subjects (10 men and 5 women, average age = 29.5 ± 8 years) and used an X-ray camera connected to a video acquisition card to obtain synchronized acoustic–radiologic data (25 images/s). The subjects were asked to swallow 10 ml of a barium suspension. Each sound component was associated with a specific position of the bolus and the anatomic structure that was moving. The average duration of the pharyngeal sound was 690 ± 162 ms. The durations of the laryngeal ascension sound and the laryngeal release sound were significantly different (72 ± 38 ms and 106 ± 47 ms, p < 0.001). The upper-sphincter opening sound was present in 100% of the recordings. Its duration was 185 ± 103 ms and was significantly different from the two other sounds. The duration of the first interval was 108 ± 44 ms and the duration of the second was 236 ± 139 ms. This study allowed us to determine the origin of the three main sound components of the pharyngeal swallowing sound with respect to movements in anatomic structures and the different bolus positions.

Comparison between 2D and 3D Local Binary Pattern Methods for Characterisation of Three-Dimensional Textures

Our purpose is to extend the Local Binary Pattern method to three dimensions and compare it with the two-dimensional model for three-dimensional texture analysis. To compare these two methods, we made classification experiments using three databases of three-dimensional texture images having different properties. The first database is a set of three-dimensional images without any distorsion or transformation, the second contains additional gaussian noise. The last one contains similar textures as the first one but with random rotations according x, y and z axis. For each of these databases, the three-dimensional Local Binary Pattern method outperforms the two-dimensional approach which has more difficulties to provide correct classifications.

Use of power law models in detecting region of interest

In this paper, we shall address the issue of semantic extraction of different regions of interest. The proposed approach is based on statistical methods and models inspired from linguistic analysis. Here, the models used are Zipf law and inverse Zipf law. They are used to model the frequency of appearance of the patterns contained in images as power law distributions. The use of these models allows to characterize the structural complexity of image textures. This complexity measure indicates a perceptually salient region in the image. The image is first partitioned into sub-images that are to be compared in some sense. Zipf or inverse Zipf law are applied to these sub-images and they are classified according to the characteristics of the power law models involved. The classification method consists in representing the characteristics of the Zipf and inverse Zipf model of each sub-image by a point in a representation space in which a clustering process is performed. Our method allows detection of regions of interest which are consistent with human perception, inverse Zipf law is particularly significant. This method has good performances compared to more classical detection methods. Alternatively, a neural network can be used for the classification phase.

A method for detecting artificial objects in natural environments

In this paper we present a method for automatic detection of man-made objects in digital images representing natural environments. This method is based on statistical distribution of texture patterns in the image. This distribution is computed using Zipfs law. The image is divided into sub-frames and Zipfs distribution is computed for each sub-frame. Then the surfaces under Zipfs plots of the different sub-frames are compared in order to determine which sub-frame contains an object. The sensitivity of this detection method to image resolution is also examined.

Zipf Analysis of Audio Signals

This paper deals with several of the possible uses of Zipf and inverse Zipf laws in the field of audio signal analysis. We show that these laws are powerful analysis tools allowing the extraction of information not available by standard methods. The adaptation of Zipf and inverse Zipf laws to audio signals requires a coding of these signals into text-like data, considered as sequences of patterns. Because these codings are of first importance since they have to bring to the fore relevant information within signals, three types of codings have been developed, depending on the representation of the audio signal it is based on: temporal, frequential and time-scale representations. Once audio signal has been coded, features linked to Zipf and inverse Zipf approaches are computed. Finally, the classification step aims at the identification of signals. Four classification methods have been considered as well as a fusion method that combines these classifiers. In order to evaluate our method, we apply it on medical acoustical signals. They occur when swallowing and contain xiphoidal sounds. The problem is to extract and characterize xiphoidal sounds according to the gastro-oesophageal reflux pathological state. The aim is to help medical doctors to characterize and diagnose this pathology, and to give, in the end, a decision help tool as efficient as possible.

Greedy Algorithm and Physics-Based Method for Active Contours and Surfaces: A Comparative Study

Deformable models, such as the discrete active contour and surface, imply the use of iterative evolution methods to perform 2D and 3D image segmentation. Among the several existing evolution methods, we focus on the greedy algorithm, which minimizes an energy functional, and the physics-based method, which applies forces in order to solve a dynamic differential equation. In this paper, we compare the greedy and physics-based approaches applied on 2D and 3D models, as regards overall speed and segmentation quality, quantified with an evaluating function mainly based on the mean distance between the model and the desired shape.

Power Law Dependencies to Detect Regions of Interest

This paper presents a novel approach to detect regions of interest in digital photographic grayscale images using power laws. The method is intended to find regions of interest in various types of unknown images. Either Zipf law or inverse Zipf law are used to achieve this detection. The detection method consists in dividing the image in several sub-images, computing the frequency of occurence of each different image pattern, representing this distribution by a power law model and classifying the sub-frames according to the power law characteristics. Both power laws models allow region of interest detection, however inverse Zipf law has better performances than Zipf law. The detection results are generally consistent with the human perception of regions of interest.

Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

Ultrasound in regional anesthesia (RA) has increased in popularity over the last years. The nerve localization presents a key step for RA practice, it is therefore valuable to develop a tool able to facilitate this practice. The nerve detection in the ultrasound images is a challenging task, since the noise and other artifacts corrupt the visual properties of such kind of tissue. In this paper we propose a new method to address this problem. The proposed technique operates in two steps. As the median nerve belongs to a hyperechoic region, the first step consists in the segmentation of this type of region using the k-means algorithm. The second step is more critical; it deals with nerve structure detection in noisy data. For that purpose, a new descriptor is developed. It combines tow methods median binary pattern (MBP) and Gabor filter to obtain the median Gabor binary pattern (MGBP). The method was tested on 173 ultrasound images of the median nerve obtained from three patients. The results showed that the proposed approach achieves better accuracy than the original MBP, Gabor descriptor and other popular descriptors.

Human Understandable Features for Segmentation of Solid Texture

The purpose of this paper is to present new texture descriptors dedicated to segmentation of solid textures. The proposed texture attributes are inspired by the human description of texture and allows a general description of texture. Moreover it is more convenient for a user to understand features signification particularly in a man-aided application. In comparison with psychological measurements for human subjects, our characteristics gave good correspondences in rank correlation of 12 different solid textures. Using these texture features, segmentation results obtained with the classical K-means method on solid textures and real three-dimensional ultrasound images of the skin are presented and discussed.

2D and 3D Deformable Models with Narrowband Region Energy

We introduce a narrow band region approach in explicit de-formable models for 2D and 3D image segmentation. Embedding a region term into the evolution process, we derive a general formulation which is applied both on a 2D parametric contour and a 3D triangular mesh. Evolution of deformable models is performed by means of energy minimization using the computationally efficient greedy algorithm. The use of a region energy related to the vicinity of the evolving surface overcomes limitations of edge-based active models while remaining time effective. Experiments with segmentation quality assessment are carried out on medical images.