Color image segmentation using saturated RGB colors and decoupling the intensity from the hue

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works

Sistema de conteo automático de células blancas en el plasma sanguíneo con información del color

Tesis de licenciatura. Aplicación de algoritmos de inteligencia artificial y tratamiento de imágenes para el conteo de células blancas en imágenes de la sangre.En el conteo automático y el reconocimiento de los sistemas de glóbulos blancos (WBC), utilizando imágenes de muestras de frotis de sangre (BS), el proceso de segmentación es una etapa importante porque la precisión del conteo y la clasificación depende, en cierta medida, de la precisión de la segmentación. Diferentes trabajos que abordan este problema se centran en algoritmos de segmentación basados en características de forma, pero estos algoritmos están diseñados, principalmente, para imágenes recortadas de WBC; es decir, la imagen de entrada contiene solo un WBC, mientras que generalmente en las imágenes BS contienen varios WBC. En este trabajo presentamos una propuesta para el conteo y reconocimiento de WBC en imágenes BS que contienen varios WBC dentro de la imagen, donde las contribuciones son: 1) una propuesta de segmentación que emula la percepción humana del color, donde los WBC están segmentados por la diferencia cromática con respecto a los otros elementos de la BS; 2) en las imágenes BS es común encontrar que los WBC están superpuestos, por lo tanto, presentamos un enfoque para separar los WBC superpuestos calculando sus diferencias de color, donde el tono y la intensidad se procesan por separado. Mostramos los resultados obtenidos al realizar experimentos con tres bases de datos de imágenes diferentes; Según los resultados obtenidos, afirmamos que nuestra propuesta es competitiva

Multi-scale molecular descriptions of human heart failure using single cell, spatial, and bulk transcriptomics

Molecular descriptions of human disease have relied on transcriptomics, the genome-wide measurement of gene expression. In the last years the emergence of capture-based technologies have enabled the transcriptomic profiling of single cells both from dissociated and intact tissues, providing a spatial and cell type specific context that complements the catalog of gene expression changes reported from bulk technologies. In the context of cardiovascular disease, these technologies open the opportunity to study the inter and intra-cellular mechanisms that regulate myocardial remodeling. In this thesis I present comprehensive descriptions of the transcriptional changes in acute and chronic human heart failure using bulk, single cell, and spatial technologies. First, I describe the creation of the Reference of the Heart Failure Transcriptome, a resource built from the meta-analysis of 16 independent studies of human heart failure transcriptomics. Then, I report the first spatial and single cell atlas of human myocardial infarction, and propose a computational strategy to identify compositional, organizational, and molecular tissue differences across distinct time points and physiological zones of damaged myocardium. Finally, I outline a methodology for the multicellular analysis of single cell data that allows for a better understanding of tissue responses and cell type coordination events in cardiovascular disease and that links the knowledge of independent studies at multiple scales. Overall my work demonstrates the importance of the generation of reliable molecular references of disease across scales