Image Analysis and Recognition
Imagery coming from high resolution, high-throughput sensors is a fundamental challenge for data-intensive science. Our focus has been on supporting image-analysis software, understanding experimental scientist needs and often times designing algorithms to efficiently deal with imaged-based experiments, getting the most of NERSC infrastructure. Advances in image-based analysis will save time between experiments, make efficient use of samples, and increase access to imaging instruments.
While we list several user-cases, how can a user get started at creating their own analysis pipelines? This page aims at kick-starting the users in using several of the available tools for understanding data at NERSC. The current topics will show toolkits for image processing and analysis and will cover common computer codes needed to perform image analysis and recognition using ImageJ and R. Please check templates for Image Processing, Analysis, and Classification using Fiji and R below.
Find a few examples to get productive using Fiji and your images: 1) Open one image: inputdir = "/global/scratch2/sd/youname/";open(inputdir + "myExperiment001.tif"); 2) Reading a collection of 2D images: numberfiles=38; filename = "myExperiment"; startfile=1;run("Image Sequence...", "open=["+inputdir+"] number="+numberfiles+" starting="+startfile+" increment=1 scale=100 file="+filename+" or= convert sort"); 3) Visualizing/snapshotting content: run("Make Montage...", "columns=9 rows=7… Read More »
Reading Images Load package ripa: > library(ripa) Read a jpeg image: > img <- readJPEG('/global/scratch2/sd/youname/cells.jpg') Read a png image: > img <- readPNG('/global/scratch2/sd/youname/'cells.png) Visualize jpeg and png images: > plot(imagematrix(img)) View from command line View from RIPA interface Read and visualize LAN (Landsat) images: > lan_img <- read.lan('/global/scratch2/sd/youname/rio.lan') > plot(imagematrix(lan_img)) View from… Read More »