Toward a better understanding of texture in vascular CT scan simulated images.

This paper shows the influence of computed tomography slice thickness on textural parameters by simulating realistic images issued from: 1) a 3D model of vascular tree, with structural and functional features and in which angiogenesis is related to the organ growth; 2) a projection/reconstruction process using fast Fourier transform. Texture analysis is performed by means of second-order statistics and gradient based methods.

Slice simulation from a model of the parenchymous vascularization to evaluate texture features: work in progress.

RATIONALE AND OBJECTIVES: To demonstrate the usefulness of a model of the parenchymous vascularization to evaluate texture analysis methods. METHODS: Slices with thickness varying from 1 to 4 mm were reformatted from a 3D vascular model corresponding to either normal tissue perfusion or local hypervascularization. Parameters of statistical methods were measured on 16128x128 regions of interest, and mean values and standard deviation were calculated. For each parameter, the performances (discrimination power and stability) were evaluated. RESULTS: Among 11 calculated statistical parameters, three (homogeneity, entropy, mean of gradients) were found to have a good discriminating power to differentiate normal perfusion from hypervascularization, but only the gradient mean was found to have a good stability with respect to the thickness. Five parameters (run percentage, run length distribution, long run emphasis, contrast, and gray level distribution) were found to have intermediate results. In the remaining three, curtosis and correlation was found to have little discrimination power, skewness none. CONCLUSION: This 3D vascular model, which allows the generation of various examples of vascular textures, is a powerful tool to assess the performance of texture analysis methods. This improves our knowledge of the methods and should contribute to their a priori choice when designing clinical studies.

Modeling of the parenchymous vascularization and perfusion.

RATIONALE AND OBJECTIVES: The authors sought to create a realistic model of the parenchymous vascularization and perfusion. METHODS: A three-dimensional vascular model has been developed that reproduces the growth process of a vascular tree (angiogenesis). This model follows physical laws related to blood flow in vessels (Poiseuille's law), takes into account anatomic constraints, and optimizes a cost function related to the blood volume. RESULTS: Vascular trees, the ramifications of which go from main arteries to small arterioles, were simulated. Vascular structures corresponding to either a normal tissue perfusion or an abnormal perfusion (for example, a local hypervascularization) were presented in three dimensions (volume rendering). Geometric and hemodynamic characteristics computed on these trees were consistent with those of real data found in the literature. The vascular model is also a good tool for studying the propagation of the contrast product in normal and abnormal vessels. CONCLUSIONS: The three-dimensional vascular model presented in this article provides insight into the simulation and the understanding of anatomic or physiologic vascular modifications.

[Analysis of texture in medical imaging. Review of the literature]. / Analyse de texture en imagerie médicale. Revue de la littérature.

This paper presents a review of the applications of texture analysis in medical imaging. Many authors take a great interest in this topic (75 papers have been published since 1984) and try to elaborate automatic methods for tissue characterization. The results are not really convincing and applications are often reduced to feasibility studies. This failure is due to the empirical approach to the problem: the first studies were performed on ultrasound images, in which visual texture is very present, but no data standardization is available with this imaging modality. A more rational approach should provide better results. For each organ or tissue, it is necessary to find the appropriate source and texture analysis method. This difficult task requires reflection concerning the interactions between tissues and imaging sources, to define judicious structuring elements. These structuring elements should facilitate the choice of the best texture analysis method, for the particular application. Considerable methodological progress has yet to be made, after which texture analysis should be a useful and efficient tool for clinical use.