IML: An image markup language

Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, 2004

WandaML is an XML-based markup language for the annotation and filter journaling of digital documents. It addresses in particular the needs of forensic handwriting data examination, by allowing experts to enter information about writer, material (pen, paper), script and content, and to record chains of image filtering and feature extraction operations applied to the data. We present the design of this format and some annotation examples, in the more general perspective of digital document annotation. Annotations may be organized in a structure that reflects the document layout via a hierarchy of document regions. WandaML can lend itself to a variety of applications, including the annotation all kinds of handwriting documents (on-line or off-line), images of printed text, medical images, and satellite images.

2010

In this paper, we present a web-based system for biomedical image storage, annotation, content-based retrieval and exploration. The system was developed using a Service Oriented Architecture (SOA) that provides easy access, interoperability and scalability. The system provides different mechanisms for searching and exploring the collection based on information retrieval, machine learning and information visualization techniques.

Journal of Digital Imaging, 2006

The integration of medical informatics and e-learning systems could provide many advanced applications including training, knowledge management, telemedicine, etc. Currently, both the domains of e-learning and medical image have sophisticated specications and standards. It is a great challenge to bring about integration. In this paper, we describe the development of a Web interface for searching and viewing medical images that are stored in standard medical image servers. With the creation of a Web solution, we have reduced the overheads of integration. We have packaged Digital Imaging and Communications in Medicine (DICOM) network services as a component that can be used via a Web server. The Web server constitutes a content repository for searching, editing, and storing Web-based medical image content. This is a simple method by which the use of Picture Archiving and Communication System (PACS) can be extended. We show that the content repository can easily interact and integrate with a learning system. With the integration, the user can easily generate and assign medical image content for e-learning. A Web solution might be the simplest way for system integration. The demonstration in this paper should be useful as a method of expanding the usage of medical information. The construction of a Web-based repository and integrated with a learning system may be also applicable to other domains.

We propose an XML-based Computer Vision Markup Language for use in Cognitive Vision, to enable separate research groups to collaborate with each other as well as making their research results more available to other areas of science and industry, without having to reveal any proprietary ideas, algorithms or even software. The Computer Vision Markup Language can communicate any type and amount of information, making unavailable functionality accessible to anyone. In this paper we introduce the language and describe how we have implemented it in a very large cognitive vision project. We provide a free open source library for working with this language, which can easily be implemented into existing code providing seamless network communication abilities and multi-platform support. Last we describe the future of CVML and how it might evolve to include other areas of research.

IJMTER, 2014

This review paper creates a bridge between MIML classification framework and Image annotation. There are generally four classification frameworks, known as Single Instance Single Label (SISL), Multi-Instance Learning (MIL), Multi-Label Learning (MLL) and Multi-Instance Multi-Label Learning (MIML). This paper introduces various classification frameworks with examples and related algorithms. An annotation is one type of metadata that can be attached to any video, image (2D/3D), text, audio and other data in the form of explanation, comments, navigation or presentational markup. This paper briefly introduces different types of annotation, annotation dataset, techniques and current research challenges in annotations

Health Informatics Journal, 2014

Sharing a medical image visually annotated by a region of interest with a remotely located specialist for consultation is a good practice. It may, however, require a special-purpose (and most likely expensive) system to send and view them, which is an unfeasible solution in developing countries such as Vietnam. In this study, we design and implement interoperable methods based on the HL7 Clinical Document Architecture and the eXtensible Markup Language Stylesheet Language for Transformation standards to seamlessly exchange and visually present the shapes of regions of interest using web browsers. We also propose a new integration architecture for a Clinical Document Architecture generator that enables embedding of regions of interest and simultaneous auto-generation of corresponding style sheets. Using the Clinical Document Architecture document and style sheet, a sender can transmit clinical documents and medical images together with coordinate values of regions of interest to reci...

International Journal of Computer Applications, 2013

Imaging has occupied a huge role in the management of patients, whether hospitalized or not. Depending on the patient's clinical problem, a variety of imaging modalities were available for use. This gave birth of the annotation of medical image process. The annotation is intended to image analysis and solve the problem of semantic gap. The reason for image annotation is due to increase in acquisition of images. Physicians and radiologists feel better while using annotation techniques for faster remedy in surgery and medicine due to the following reasons: giving details to the patients, searching the present and past records from the larger databases, and giving solutions to them in a faster and more accurate way. However, classical conceptual modeling does not incorporate the specificity of medical domain specially the annotation of medical image. The design phase is the most important activity in the successful building of annotation process. For this reason, we focus in this paper on presenting the conceptual modeling of the annotation of medical image by defining a new profile using the StarUML extensibility mechanism.

Workshop on Semantic Web Applications and Tools for Life Sciences, 2014

Annotopia is an open source, open services platform for creating, managing, manipulating and sharing open annotation using the W3C Open Annotation Data Model. It can create and/or manage annotation of HTML, PDF, and other resources including data and ontology concepts, with text, semantic tags, and other annotation types. It supports fine-grained permissions on annotations. Annotopia is a Swiss-army knife for W3C Open Annotation system developers, eliminating many otherwise challenging backend development tasks.

Lecture Notes in Computer Science, 2009

We present a tool for semantic medical image annotation and retrieval. It leverages the MEDICO ontology which covers formal background information from various biomedical ontologies such as the Foundational Model of Anatomy (FMA), terminologies like ICD-10 and RadLex and covers various aspects of clinical procedures. This ontology is used during several steps of annotation and retrieval: (1) We developed an ontology-driven metadata extractor for the medical image format DI-COM. Its output contains, e. g., person name, age, image acquisition parameters, body region, etc. (2) The output from ( ) is used to simplify the manual annotation by providing intuitive visualizations and to provide a preselected subset of annotation concepts. Furthermore, the extracted metadata is linked together with anatomical annotations and clinical findings to generate a unified view of a patient's medical history. (3) On the search side we perform query expansion based on the structure of the medical ontologies. (4) Our ontology for clinical data management allows us to link and combine patients, medical images and annotations together in a comprehensive result list. (5) The medical annotations are further extended by links to external sources like Wikipedia to provide additional information. 1