Application of Bidimensional Empirical Mode Decomposition for Particle Identification and Size Determination

The analysis of surface texture appears in different disciplines of science and technology. Surface texture is generally multiscale and can be separated into different spatial frequency or wavelength ranges providing information on image characteristics such as shape, roughness, pseudoperiodic components and chaotic components. Surface texture translates into image texture. Textures in images are complex visual patterns composed of entities or subpatterns that have characteristic brightness, color, slope, size, etc. In this work, we address the analysis of multimodal images and their decomposition using the bidimensional empirical mode decomposition. This approach allows us to obtain component images from each original image, each of them with a spatial frequency range. These analysis methods are currently used in images from various disciplines such as biology (analysis of biological tissues), environmental and health sciences (particulate matter dispersed in the atmosphere), materials sciences (texture on surfaces), earth sciences (SAR images). The main objective is to present an algorithm that allows identifying, segmenting, and classifying structures and morphologies in each image mode. The proposed numerical technique is applied to images from cytology analysis to study number of particles present in fibroma (benign tumor) nuclei compared to the number in sarcoma (malignant tumor) nuclei in order to investigate if there is a significant difference between them, sufficient to use this fact as part of a diagnosis.