A medical image object-oriented database with image processing and automatic reorganization capabilities

Aubry, Florent; Chameroy, V.; Paola, R. Di

doi:10.1016/s0895-6111(96)00022-5

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…These requirements serve as input of the design modeling of the MIDB, the next phase of MIDB design. We plan the use an object-oriented approach to perform this conceptual modeling [26]. To be compliant with commercial systems for digital mammography and CAD mammography, terminological systems used by the MIDB to describe and index data must be based on DICOM and BI-RADS dictionaries.…”

Section: Discussionmentioning

confidence: 99%

<title>Crews-l'Ecritoire analysis for the implementation of a medical image database for mammography</title>

Demigha

Rolland

Baum³

et al. 2001

SPIE Proceedings

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Section: Discussionmentioning

confidence: 99%

<title>Crews-l'Ecritoire analysis for the implementation of a medical image database for mammography</title>

Demigha

Rolland

Baum³

et al. 2001

SPIE Proceedings

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“…Relevant image identification would be a simple process if an automated segmentation/classification algorithm was available that worked accurately on a wide variety of images;12 however, such an algorithm is not yet a reality. Several groups have suggested a knowledge‐based approach that can customize the image analysis task using object‐centered hierarchical planning13–15 to isolate key regions, but this approach is still ultimately manual, since it requires expert input to guide the process. A survey of the imaging informatics and image processing literature identified one study that could potentially be modified to locate relevant images (image slices that contain the abnormality) 16.…”

Section: Cornerstone 1: Developing Methods For Acquisition and Presenmentioning

confidence: 99%

A Review of Medical Imaging Informatics

Sinha

Bui

Taira

et al. 2002

Annals of the New York Academy of Sciences

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This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content-based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general-purpose time-based multimedia data models, health-care-specific data models, knowledge models, and problem-centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer-to-peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem-specific data organization, including our problem-centric, context and user-specific visualization interface.

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“…Image processing is now playing a leading role in many fields such as satellite (Bristor, ; Gorgan et al, ), optic (Buxtona and Buxton, ; Heid and Kääb, ) and medical (Aubry et al, ; Arsalan et al, ) imaging. The latest one is the area in which we are focusing our studies.…”

Section: Introductionmentioning

confidence: 99%

A new images segmentation method based on modified particle swarm optimization algorithm

Hamdaoui

Ladgham

Sakly

et al. 2013

Int J Imaging Syst Tech

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ABSTRACT:The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues.

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A medical image object-oriented database with image processing and automatic reorganization capabilities

Cited by 14 publications

References 10 publications

<title>Crews-l'Ecritoire analysis for the implementation of a medical image database for mammography</title>

<title>Crews-l'Ecritoire analysis for the implementation of a medical image database for mammography</title>

A Review of Medical Imaging Informatics

A new images segmentation method based on modified particle swarm optimization algorithm

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