Implementation of web-based medical image retrieval system in Oracle

Dimitrovski, Ivica; Guguljanov, Pero; Loskovska, Suzana

doi:10.1109/icastech.2009.5409726

Cited by 12 publications

(17 citation statements)

References 7 publications

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“…There are many factors to consider in the design of a CBIR systems based on the domains and purposes, choice of right features, similarity measurement criteria, indexing mechanism, and query formulation technique [13,19]. The most important factors in the design process is the choice of suitable visual features and the methodologies to extract them from raw images, as it affects all other subsequent processes.…”

Section: B Content Based Image Retrievalmentioning

confidence: 99%

Study of Medical Image Retrieval

Bhadoria¹,

Dethe

2010

2010 International Conference on Data Storage and Data Engineering

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Abstrac-Among the applications of computer science in the field of medicine, the processing of medical image data is playing an increasingly important role. With medical imaging techniques such as X-Ray, computer tomography, magnetic resonance imaging, and ultrasound, the amount of digital images that are produced in hospitals is increasing incredibly. Thus the need for systems that can provide efficient retrieval of images of particular interest is becoming very high. Unfortunately, only very few medical image retrieval systems are currently used in clinical routine. This paper presents a highlight on the role of image retrieval in medical domain. Also it reviews the techniques used in medical image. Paper has also briefed about retrieval trends, key issues and limitations. It also point out scope and challenges in designing image retrieval systems.

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Section: B Content Based Image Retrievalmentioning

confidence: 99%

Study of Medical Image Retrieval

Bhadoria¹,

Dethe

2010

2010 International Conference on Data Storage and Data Engineering

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“…Another CBIR system for medical images is CasImage, which has been integrated into a PACS environment, containing a teaching and reference database and the medGIFT retrieval system [19] [21]. The images stored in the image database are obtained from different modalities such as MRI, CT, radiographs.…”

Section: One Such System That Uses Images By High Resolution Computedmentioning

confidence: 99%

“…The process behind IRMA includes categorization, registration, feature extraction, feature selection, indexing, identification and retrieval [20] [21].…”

Section: One Such System That Uses Images By High Resolution Computedmentioning

confidence: 99%

Content based image retrieval in medical applications: an improvement of the two-level architecture

Trojacanec¹,

Dimitrovski²,

Loskovska

2009

Ieee Eurocon 2009

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Medical imaging is specific part of medicine according to the medical images features. This is the reason for growing up requirements for improvement of Content Based Image Retrieval (CBIR) systems applied on medical images. Improving these systems is one of the biggest challenges for scientists in this domain. In this paper we propose an extension and improvement of the existing two-level CBIR architecture proposed in [1]. The proposed improvement of the two-level architecture is expected to increase the accuracy of the retrieval results. Index Terms: ContentBased Image Retrieval (CBIR), Digital Imaging and Communications in Medicine (DICOM), archiving and communication systems (PACS), medical images.

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“…This paper includes the comparison of color based and texture based image retrieval techniques with the proposed fuzzy connectedness image segmentation with geometric moments for retrieval of various medical modality images including angiograms, ultrasound and xray. The comparison is carried out between proposed approach and various image retrieval approaches such as web based medical image retrieval in oracle [20], pattern similarity based medical image retrieval [6], indexing for relevance feedback in image retrieval [3], [8]- [9], entropy based image retrieval [11], similarity based online feature selection [10], entropy based feature selection [13], [14] Manuscript received September 1, 2014; revised November 24, 2014.…”

Section: Introductionmentioning

confidence: 99%

PET medical image retrieval using fuzzy connectedness image segmentation and geometric moments

Bhagat¹,

Atique²

2015

Computer Science and Systems Engineering

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In medical imaging DICOM (Digital Imaging and Communications in Medicine) format is the most commonly used format. Various medical imaging sources generate images in this format, which are collected in large database repository [1]. Various modalities of medical images such as CT scan, X-Ray, Ultrasound, Pathology, MRI, Microscopy, etc [2] are used to collect these images. From the analysis of these medical images proper diagnosis of different diseases can provided to the patients. This paper presents an approach for efficient image retrieval of angiograms, ultrasound and x-ray medical images from the huge medical image datasets. This paper presents the proposed fuzzy connectedness image segmentation with geometric moment approach which provides more precise retrieval results with less computational complexity. This paper compares the various techniques for DICOM medical image retrieval and shows that the proposed fuzzy connectedness image segmentation with geometric moments based image feature extraction and image retrieval approach performs better as compared to other approaches. The proposed method produced results with the precision of 95%. 

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Implementation of web-based medical image retrieval system in Oracle

Cited by 12 publications

References 7 publications

Study of Medical Image Retrieval

Study of Medical Image Retrieval

Content based image retrieval in medical applications: an improvement of the two-level architecture

PET medical image retrieval using fuzzy connectedness image segmentation and geometric moments

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