Texture descriptor approaches to level set segmentation in medical images

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