BIGS - Big Image Data Analysis Toolkit

BIGS is a framework for large scale image processing and machine learning by using computing power in parallel and designed with the following principles:

- Simplicity for the experimenter
- Simplicity for the algorithm developer
- Harness computing power whenever is available, whether on desktops, computer labs, clusters or the cloud

BIGS is designed to exploit coarse grained paralellism through data partitioning and is founded upon two main concepts:

Pipelines, defined by the experimenter, specifying a chain of data processing operations to be performed on input data (images, vectors).
Workers: BIGS agents in charge of resolving the computational load required by pipelines. 
Workers can be deployed over desktops, computer labs, clusters, or cloud resources.Workers can be added or removed anytime to an on-going computation. 

Computing resources need not be committed in advance to any computation, and can join as they become available. 

BiMed - Content-Based Retrieval in a Histology Image Database

BiMed is a biomedical search engine that provides access to a collection of 20,000 images of histology to the study of the four fundamental living being tissues. Development site

This was developed in the project "System for Content-Based Retrieval in a Medical Image Database". It provides access to a collection of 20,000 images of histology for the study of fundamental tissues. Due to the large amount of images, the system offers search tools for textual and visual content. When the user finds a picture of his/her interest, he can ask the system to locate the most similar images according to visual similarity criteria, such as colors, edges or textures. This tool is useful for research and teaching in biomedical area, taking into account that it offers access to a vast collection of images illustrating different tissues Usually the reference material for the study of histology contains a few examples and pictures illustrating structures. Moreover, these images are found only in books and scientific articles, and finding images in the printed material can be time consuming because it must scan multiple pages from different sources manually. There are some collections of histological images published on the Internet that do not exceed 100 or 200 illustrations. Access to a comprehensive collection of nearly 20,000 images of histology as reference material is of great importance to academics and research in biology and medicine to train students in visual and histology for the recognition of different forms of tissue structures such as cutting and increased staining. The tool allows free exploration by users looking for textual and visual concepts.