Automation Process for Morphometric Analysis of Volumetric CT Data from Pulmonary Vasculature in Rats

With advances in medical imaging scanners, it has become commonplace to generate large multidimensional datasets. These datasets require tools for a rapid, thorough analysis. To address this need, we have developed an automated algorithm for morphometric analysis incorporating A Visualization Workshop computational and image processing libraries for three-dimensional segmentation, vascular tree generation and structural hierarchical ordering with a two-stage numeric optimization procedure for estimating vessel diameters. We combine this new technique with our mathematical models of pulmonary vascular morphology to quantify structural and functional attributes of lung arterial trees. Our physiological studies require repeated measurements of vascular structure to determine differences in vessel biomechanical properties between animal models of pulmonary disease. Automation provides many advantages including significantly improved speed and minimized operator interaction and biasing. The results are validated by comparison with previously published rat pulmonary arterial micro-CT data analysis techniques, in which vessels were manually mapped and measured using intense operator intervention

Acoustical structured illumination for super-resolution ultrasound imaging.

Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges

An Image Registration Method for Head CTA and MRA Images Using Mutual Information on Volumes of Interest

Image registration is an important and a fundamental task in computer vision and image processing field. For example, to make a surgical plan for head operation, the surgeons should gain more detailed information from CT angiography (CTA) and MR angiography (MRA) images. And the abnormalities can be easily detected from the fusion image which is obtained from two different modalities. One of the multiple modal image registration methods is matching the CTA and MRA, by which the image of head vascular could be enhanced. In general, the procedure for fusion is completed manually. It is time-consuming and subjective. Particularly the anatomical knowledge is required as well. Therefore, the development of automatic registration methods is expected in medical fields. In this paper, we propose a method for high accurate registration, which concentrates the structure of head vascular. We use 2-D projection images and restrict volume of interests to improve the processing affection. In experiments, we performed our proposed method for registration on five sets of CTA and MRA images and a better result from our previous method is obtained.SCIS&ISIS 2014 : Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent, December 3-6, 2014, Kitakyushu, Japa

Image processing platform for the analysis of brain vascular patterns

Aquest projecte consisteix en el desenvolupament d'una aplicació web per al suport metge de l'anàlisi d'imatges cerebrovasculars. L'objectiu és crear un prototip obert i modular que serveixi com a exemple i plantilla per al desenvolupament d'altres projectes. L'objectiu és aconseguir una alternativa a les opcions comercials actualment existents d'eines d'anàlisi de dades en la indústria de la salut. L'aplicació es desenvolupa utilitzant el llenguatge Python. L'aplicació permet a l'usuari carregar imatges mèdiques contingudes en fitxers DICOM, aquestes imatges són processades per eliminar el soroll i extreure els vasos sanguinis de la imatge de cara a l'anàlisi. Els resultats es resumeixen en tres gràfics: un anomenat mapa isocronal que reflecteix l'evolució temporal de el flux de la sang, un altre gràfic mostrant l'esquelet de l'estructura o xarxa de sistema vascular, i un últim gràfic que representa dades numèriques extretes com a paràmetres de l'anàlisi de l'esquelet. El framework Dash és usat per implementar la interfície i la interacció amb l'usuari. L'usuari pot carregar dues mostres diferents a el mateix temps i executar una anàlisi per comparar els resultats de les dues mostres en una mateixa pantalla. Finalment l'aplicació s'empaqueta en un contenidor virtual usant la plataforma Docker. Després de provar l'aplicació amb imatges reals de mostra proporcionades per l'Hospital Sant Joan de Déu, els resultats obtinguts són satisfactoris ja que l'aplicació funciona adequadament així com els algoritmes de processat d'imatge aplicats. Malgrat les limitacions de el projecte, el treball realitzat pot servir com a punt de partida per a futurs desenvolupaments.Este proyecto consiste en el desarrollo de una aplicación web para el soporte médico del análisis de imágenes cerebrovasculares. El objetivo es crear un prototipo abierto y modular que sirva como ejemplo y plantilla para el desarrollo de otros proyectos. El objetivo es conseguir una alternativa a las opciones comerciales actualmente existentes de herramientas de análisis de datos en la industria de la salud. La aplicación se desarrolla usando el lenguaje Python. La aplicación permite al usuario cargar imágenes médicas contenidas en ficheros DICOM, esas imágenes son procesadas para eliminar el ruido y extraer los vasos sanguíneos de la imagen de cara al análisis. Los resultados se resumen en tres gráficos: uno llamado mapa isocronal que refleja la evolución temporal del flujo de la sangre, otro gráfico mostrando el esqueleto de la estructura o red del sistema vascular, y un último gráfico que representa datos numéricos extraídos como parámetros del análisis del esqueleto. El framework Dash es usado para implementar la interfaz y la interacción con el usuario. El usuario puede cargar dos muestras diferentes al mismo tiempo y ejecutar un análisis para comparar los resultados de las dos muestras en una misma pantalla. Finalmente la aplicación se empaqueta en un contenedor virtual usando la plataforma Docker. Tras probar la aplicación con imágenes reales de muestra proporcionadas por el Hospital Sant Joan de Déu, los resultados obtenidos son satisfactorios ya que la aplicación funciona adecuadamente así como los algoritmos de procesado de imagen aplicados. Pese a las limitaciones del proyecto, el trabajo realizado puede servir como punto de partida para futuros desarrollos.This project consists in the development of a web application for the support of medical professionals in the analysis of cerebrovascular image data. The objective is to build an open and modular prototype that can serve as an example or template for the development of other projects. The purpose is to have an open alternative to the commercial options currently available for data analysis tools in the health industry market. The application is developed using Python. The application allows the user to load medical images contained in DICOM files, those images are processed for noise removal and binarization in order to build the result graphs. The results are three graphs: an image graph called “isochronal map” reflecting the temporal evolution of the blood flow, an image graph showing the skeleton of the vascular system structure, a box-plot graph representing the numerical branch data extracted from the skeleton. The Dash framework is used to construct the user interface and to implement the user interaction functionalities. The subject can load two different samples at the same time and execute the analysis to compare the results for both samples in the same screen. Finally the application is containerized using Docker to package it and make it multi-platform. The app is tested and the results are satisfactory as the resulting application works properly and so do the image processing algorithms for the input data provided by the Hospital Sant Joan de Déu. Despite its obvious limitations, the work done serves as a starting point for future developments

Segmentation of Vascular Structures and Hematopoietic Cells in 3-D Microscopy Images and Quantitative Analysis

In this paper, we present image processing methods for quantitative study of how the bone marrow microenvironment changes (characterized by altered vascular structure and hematopoietic cell distribution) caused by diseases or various factors. We develop algorithms that automatically segment vascular structures and hematopoietic cells in 3-D microscopy images, perform quantitative analysis of the properties of the segmented vascular structures and cells, and examine how such properties change. In processing images, we apply local thresholding to segment vessels, and add post-processing steps to deal with imaging artifacts. We propose an improved watershed algorithm that relies on both intensity and shape information and can separate multiple overlapping cells better than common watershed methods. We then quantitatively compute various features of the vascular structures and hematopoietic cells, such as the branches and sizes of vessels and the distribution of cells. In analyzing vascular properties, we provide algorithms for pruning fake vessel segments and branches based on vessel skeletons. Our algorithms can segment vascular structures and hematopoietic cells with good quality. We use our methods to quantitatively examine the changes in the bone marrow microenvironment caused by the deletion of Notch pathway. Our quantitative analysis reveals property changes in samples with deleted Notch pathway. Our tool is useful for biologists to quantitatively measure changes in the bone marrow microenvironment, for developing possible therapeutic strategies to help the bone marrow microenvironment recovery

GIMP and Wavelets for Medical Image Processing: Enhancing Images of the Fundus of the Eye

The visual analysis of retina and of its vascular characteristics is important in the diagnosis and monitoring of diseases of visual perception. In the related medical diagnoses, the digital processing of the fundus images is used to obtain the segmentation of retinal vessels. However, an image segmentation is often requiring methods based on peculiar or complex algorithms: in this paper we will show some alternative approaches obtained by applying freely available tools to enhance, without a specific segmentation, the images of the fundus of the eye. We will see in particular, that combining the use of GIMP, the GNU Image Manipulation Program, with the wavelet filter of Iris, a program well-known for processing astronomical images, the result is giving images which can be alternative of those obtained from segmentation.Comment: Keywords: Image processing, Retina, Retina Vessels, GIMP, AstroFracTool, Iris, Wavelet

Volumetric microvascular imaging of human retina using optical coherence tomography with a novel motion contrast technique

Phase variance-based motion contrast imaging is demonstrated using a spectral domain optical coherence tomography system for the in vivo human retina. This contrast technique spatially identifies locations of motion within the retina primarily associated with vasculature. Histogram-based noise analysis of the motion contrast images was used to reduce the motion noise created by transverse eye motion. En face summation images created from the 3D motion contrast data are presented with segmentation of selected retinal layers to provide non-invasive vascular visualization comparable to currently used invasive angiographic imaging. This motion contrast technique has demonstrated the ability to visualize resolution-limited vasculature independent of vessel orientation and flow velocity