A New Framework for Structured Reporting Bio Medical Images

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1 A New Framework for Structured Reporting Bio Medical Images Shubh Chawla 1, Sunil Ahuja 2 1 M. Tech. Scholar, Doon Valley Engineering College, Haryana, India, shubh_chawla@yahoo.com 2 Assistant Professor, Doon Valley Engineering College, Haryana, India, ahujaksunil@gmail.com Abstract Medical imaging is the technique and process used to create images of the human body or for clinical purposes or medical sciences. Recently, the digital imaging and communications in medicine (DICOM) standard introduced rules for the encoding, transmission, and storage of imaging diagnostic report. This medical document can be stores and communicated with the images in picture archiving and communication system (PACS). DICOM structured document that contains text with links to other data like images, waveforms, and spatial or temporal coordinate. Its structure, along with its wide use of code information, enables the semantic understanding of the data that is essential for the Electronic Healthcare Record deployment. This Research work is about Structured Reporting in DICOM medical format, the digital imaging and communications in medicine (DICOM) standard presented rules for the encoding, transmission and storage of the imaging analytical report. Keywords: Medical Imaging, DICOM Standard, Structured Reporting *** INTRODUCTION Medical imaging is the technique and process used to create images of the human body or for clinical purposes or medical sciences. Medical imaging is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. Biomedical imaging technologies utilizes either x-rays (CT scans), sound (ultrasound), magnetism (MRI), radioactive pharmaceuticals (nuclear medicine: SPECT, PET) or light (endoscopy, OCT) to assess the current condition of an organ or tissue and can monitor a patient over time over time for diagnostic and treatment evaluation. Since the discovery of X-rays by Wilhelm Conrad Rontgen in 1895, medical images have become a major components of diagnostic, treatment planning and procedures or follow-up studies. Furthermore, medical images are used for education, documentation and research describes morphology as well as physical and biological functions in 1D, 2D, 3D, and even 4D image data (e.g., cardiac MRI, where up to eight volumes are acquired during a single heart cycle). Today, a large variety ofimaging modalities have been established, which are based on mediation, reflection or refraction of light, radiation, temperature, resonance, or spin. Table-1:Modalities of Medical Images Image processing methods developed for image segmentation, computer assisted detection, to reduce noise and distortion, and to provide advanced visualization to enhance the abnormalities within images. As radiology has evolved from images recorded and viewed on film to being stored, retrieved, and manipulated electronically, radiologists have been confronted with challenges that have created new imperatives for imaging informatics. Computational Challenges in Medical Imaging: Challenges such as geometrical registration of images of differing modality, for example lining up a CAT scan with an MRI image, are being undertaken with a powerful blend of applied Mathematical and Computational Resources: Computational requirements of biomedical computing, namely The CPU,storage, software and connectivity requirements needed to digest, exploit and archive the images produced by the clinical and research biomedical imaging communities. Special Issue June-2014, Paper id - IJRETM-2014-SP-029 1

2 Computational Challenges are: Inadequate CPU cycles Computer-assisted qualitative analysis Connectivity: Access to remote data Full quantitative analysis Fused/Merged Images Merged DB structures: images+ parameters Query/display tools, access to distributed data Insufficient data storage Visualization: Meaningful and useful 1.1Overview of the DICOM Standard The vast majority of digital medical images are stored and manipulated as Digital Imaging and Communications in Medicine (DICOM) standard objects. DICOM contains a large amount of metadata about whom, how and when the images were acquired. It also specifies precisely how the information is stored in the image object and how pixels should be interpreted for display. In a DICOM series of images from one patient at a first point in time and a second. DICOM series of images from the same patient at a second time point. DICOM files can be exchanged between two entities that are capable of receiving image and patient data in DICOM format. Digital Imaging and Communication in Medicines is a standard for handling, storing, printing, and transmitting data in medical imaging. DICOM files can be interchange between two entities that are capable of receiving image and patient data in DICOM format. DICOM defines network oriented services for transfer or printing of the images, media formats for data interchange, work-flow management, consistency and quality of presentation and requirements of conformance of devices and programs.the digital imaging and communications in medicine (DICOM) standard presented rules for the encrypting, transmission, and storage of the imaging analytical report. Goals of the DICOM standard The DICOM Standard assists interoperability of devices suing conformance. In particular, it: Addresses the semantics of Commands and related data. For devices to interact, there must be standards on how devices are expected to react to Commands and supplementary data, not just the information which is to be relocated between devices; Addresses the semantics of file facilities, file formats and information indexes necessary for off-line communication; Is explicit in describing the conformance requirements of executions of the Standard. In particular, a conformance report must specify enough information to determine the purposes for which interoperability can be estimated with another device claiming conformance. Enables operation in a networked situation. Is planned to accommodate the outline of new facilities, thus simplifying support for future medical imaging presentations. Makes use of usual international standards wherever relevant, and itself conforms to traditional documentation plans for universal standards. 1.2 Structured Reporting (SR) Structured Reporting is a structured document that contains text with links to other data like images, waveforms and spatial or temporal coordinates. Its structure, along with its widely use of coded information enables the semantic understanding of the data that is essential for electronic patient data. The content of DICOM SR is structured hierarchically like a tree. The actual information lies in a tree node. A node contains a specific type of information. Each node is called a content item; it has children that are nodes themselves. Effective searching and matching in structured reporting also requires consistency in the use of terms for concepts and values. DICOM uses coded Entries. A coded entry in DICOM consists of Code value - that specifies the term Coding scheme designator - that specifies the dictionary that defines the term Code meaning - that contains a human readable description of the term. From the viewpoint of DICOM Structured Reporting (SR), what merges these different opinions are: The occurrence of lists and hierarchical connections. The practice of coded or numeric content in addition to plain text. The usage of relationships among concepts. The presence of entrenched references to images and entities. Special Issue June-2014, Paper id - IJRETM-2014-SP-029 2

3 Fig-1:.Simple example of a DICOM Structured Report In DICOM SR, each document encrypts only what is meant, not how it is planned to be displayed, printed or else presented. The significance is required to be unmistakable. It cannot be reliant on suppositions about how it might be accessible. DICOM SR may be equated to XML (extensible Markup Language) as differing to HTML (Hypertext Markup Language).Whereas HTML expresses explicit production information without significance, XML covers tags with meaning but without obvious or implicit presentation. Just as it may be essential to combine an XML article with some form of demonstration tool such as a style sheet transcribed in XSL (extensible Stylesheet Language) or CSS (Cascading Style Sheets) in order to condense it, so it may be needed to use an application that recognizes how to change DICOM SR content into an suitable form. Trees and Relationships In DICOM SR, all content items are encrypted as a single tree with a only root content item. The root content items passage the document title. Its children and their offspring express the content of the document. An SR may be as simple as a flat list of content items beneath the document title. Alternatively, an SR may be as compound as a directed acyclic graph (DAG). In a DAG, even though content is encrypted as a tree, individual content items may mention content items former than their parents or children. References to inner nodes within the tree of a different SR document are not permitted. The content tree must be fully contained within a single SR document. Fig-2: An SR Tree with References (Directed Acyclic Graph) 1.3 Report Management Structured reporting needs more than creating, encrypting and storing content. Once created, documents must be achieved so that they are available where and when they are required. One desires to define where documents come from, when to create them, where they are sent, what version of a document is in use, what the current state of a document is, and how to locate appropriate documents. The SR IOD describes a module detached from the content tree that contain this statistics (the SR Document General Module). Structured reports are DICOM composite instances just like images and waveforms. As such, they contain attributes to recognize and define the entities in the composite information model. This contains information about the patient study, study component, series and instance. Since SR documents are examples of composite SOP classes they are allocated unique identifiers (UIDs). DICOM composite instances are usually persistent beyond the space of their transmission; they are documents relatively than messages. In a message-oriented paradigm, they can be transient and be unwanted after they have been used. Special Issue June-2014, Paper id - IJRETM-2014-SP-029 3

4 2. RESULTS AND ANALYSIS DICOM SR ALGORITHM First of all we read the DICOM files from DICOM Data file using DICOM Read Function then Split the imported DICOM file into imaging component imc and Patient Data Dictionary PDD. Define the DICOM DTD for DICOM Structured Reporting using XML. Fig-3: Flowchart for DICOM SR The Algorithm progresses by copying each element of the DICOM Data element (PDD) into node. Initialize the DICOM SR Structure using DOM Elements. The Algorithm Selects node element which is to be reported in SR Document and appends the XML Structure. At the end, the algorithm links imaging component imc and SR. Export generated Structured Report into XML file by using XML DTD. The complete algorithm is described below: ALGORITHM 1. DICOM= read DICOM data from file; 2. [imc;pdd]= split DICOM data into imaging component and patient data dictionary 3. DICOM DTD define XML-DTD for DICOM SR 4. define XML structure for imported DICOM format // initialize nodes 5. NODES:= extract nodes from PDD 6. XNODES:= initialize DICOM SR structure 7. for each node in NODES: XNODE:= select respective XML node using XML-DOM[ XNODE.name= node;name XNODE.value=node;value] 8. Append XNODE into XML-SR 9. End; 10. link imc with XNODES into XML-SR 11. export XML-SR to XML using XML-DTD Special Issue June-2014, Paper id - IJRETM-2014-SP-029 4

5 IMPLEMENTATION RESULTS Fig -4: DICOM Imaging Component of a Patient X-Ray Fig-5:.Resulting DICOM SR in a GUI developed for DICOM SR 3. CONCLUSIONS Main challenges that were tackled by this work included: Creation of a new framework for structured reporting of DICOM images was developed. The system helped integrate heterogeneous system and provide clinical image access services. We were able to provide DICOM compatible format for analysis using XML. The Framework can be used in scenarios where no DICOM based device is available for knowing something about the DICOM images, the Framework can help many DICOM standard prototypes that have been later defined to constrain the possible structures and to provide some basic codes that can be used to encode specific reports such as breast imaging or vascular ultrasound processes reports. 4. REFERENCES [1] Ségonne, Florent. "Segmentation of medical images under topological constraints." PhD diss., Massachusetts Institute of Technology, Special Issue June-2014, Paper id - IJRETM-2014-SP-029 5

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