Periodic variations in patterns within a group of pixels provide important information about the surface of interest and can be used to identify objects or regions. Hence, a proper analysis can be applied to extract particular features according to some specific image properties. Recently, texture analysis using orthogonal polynomials has gained attention since polynomials characterize the pseudo-periodic behavior of textures through the projection of the pattern of interest over a group of kernel functions. However, the maximum polynomial order is often linked to the size of the texture, which implies in many cases, a complex calculation and introduces instability in higher orders leading to computational errors. In this paper, we address this issue and explore a pre-processing stage to compute the optimal size of the window of analysis called “texel.” We propose Haralick-based metrics to find the main oscillation period, such that, it represents the fundamental texture and captures the minimum information, which is sufficient for classification tasks. This procedure avoids the computation of large polynomials and reduces substantially the feature space with small classification errors. Our proposal is also compared against different fixed-size windows. We also show similarities between full-image representations and the ones based on texels in terms of visual structures and feature vectors using two different orthogonal bases: Tchebichef and Hermite polynomials. Finally, we assess the performance of the proposal using well-known texture databases found in the literature.
Medical image analysis has become an important tool for improving medical diagnosis and planning treatments. It involves volume or still image segmentation that plays a critical role in understanding image content by facilitating extraction of the anatomical organ or region-of-interest. It also may help towards the construction of reliable computer-aided diagnosis systems. Specifically, level set methods have emerged as a general framework for image segmentation; such methods are mainly based on gradient information and provide satisfactory results. However, the noise inherent to images and the lack of contrast information between adjacent regions hamper the performance of the algorithms, thus, others proposals have been suggested in the literature. For instance, characterization of regions as statistical parametric models to handle level set evolution. In this paper, we study the influence of texture on a level-set-based segmentation and propose the use of Hermite features that are incorporated into the level set model to improve organ segmentation that may be useful for quantifying left ventricular blood flow. The proposal was also compared against other texture descriptors such as local binary patterns, Image derivatives, and Hounsfield low attenuation values.
In recent years, the use of Magnetic Resonance Imaging (MRI) to detect different brain structures such as
midbrain, white matter, gray matter, corpus callosum, and cerebellum has increased. This fact together with
the evidence that midbrain is associated with Parkinson’s disease has led researchers to consider midbrain
segmentation as an important issue. Nowadays, Active Shape Models (ASM) are widely used in literature for
organ segmentation where the shape is an important discriminant feature. Nevertheless, this approach is based
on the assumption that objects of interest are usually located on strong edges. Such a limitation may lead to a
final shape far from the actual shape model. This paper proposes a novel method based on the combined use
of ASM and Local Binary Patterns for segmenting midbrain. Furthermore, we analyzed several LBP methods
and evaluated their performance. The joint-model considers both global and local statistics to improve final
adjustments. The results showed that our proposal performs substantially better than the ASM algorithm and
provides better segmentation measurements.
This paper describes a segmentation method for time series of 3D cardiac images based on deformable models. The goal
of this work is to extend active shape models (ASM) of
tree-dimensional objects to the problem of 4D (3D + time)
cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that
encodes the spatio-temporal variability of a training set, i.e., the principal modes of variation of the temporal shapes are
computed using some statistical parameters. An active search is used in the segmentation process where an initial
approximation of the spatio-temporal shape is given and the gray level information in the neighborhood of the landmarks
is analyzed. The starting shape is able to deform so as to better fit the data, but in the range allowed by the point
distribution model. Several time series consisting of eleven 3D images of cardiac CT are employed for the method
validation. Results are compared with manual segmentation made by an expert. The proposed application can be used
for clinical evaluation of the left ventricle mechanical function. Likewise, the results can be taken as the first step of
processing for optic flow estimation algorithms.