CT scans with neurocysticercosis in epileptics patients: a computer-based method for detection and quantification

Comunello, Eros; Wangenheim, Aldo von; Barreto, Jorge Muniz; Borges, Paulo S. S.; Wille, Paulo Roberto; Bittencourt, Paulo Cesar Trevisol; Krechel, Dirk; Faber, Kerstin

doi:10.1109/cbms.1999.781246

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Image analysis knowledge plays an important role in several, very different applications: First of all, in the system to detect early breast cancer in contrast enhanced MR sequences, described in Section 4.3. But it is also momentous in a system to reconstruct and measure aortic aneurysms for the design of personalized endoluminal prostheses [5], and in an application to detect and quantify Neurocysticercosis in CT scans of epileptic patients [6].…”

Section: Resultsmentioning

confidence: 99%

A framework for radiological assistant systems

Krechel

Bergmann²,

Maximini³

et al.

Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)

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The market for health care systems supporting physicians and improving their daily routine is dynamically growing. The development of these systems makes great demands on the handling of medical knowledge like anatomical and process knowledge. In this paper a special approach for the radiological domain will be presented. Our framework includes mechanisms to store medical knowledge in different knowledge containers, whose importance is varying from application to application, and to support the execution of the processes. Three applications are introduced exemplary. We analyzed the application scenarios to find the knowledge intensive tasks, that can be supported by an assistant system. The implemented solutions are integrated in daily work of our radiological partner hospitals.

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Section: Resultsmentioning

confidence: 99%

A framework for radiological assistant systems

Krechel

Bergmann²,

Maximini³

et al.

Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)

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“…We also have developed solutions for very special problems, amongst others a system to detect breast cancer in contrast enhanced MR sequences early [5], a system to reconstruct and measure aortic aneurysms for the design of personalized endoluminal prostheses [11], and an application to detect and quantify Neurocysticercosis in CT scans of epileptic patients [12]. All these systems are used in the daily routine of our partner hospitals and clinics in Germany and Brazil, because they are connected with CyclopsDistMedDB, our transparent gateway for distributed data access in DICOM 1 format [13].…”

Section: Motivationmentioning

confidence: 99%

Integrating CBR into the health care organization

Alexandrini

Krechel²,

Maximini

et al.

16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings.

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The market for health care systems supporting physicians and improving their daily routine is steadily growing. The development of these systems requires handling medical knowledge and process knowledge. One promising knowledge management technique that has been applied in various medical assistant systems is Case-Based Reasoning (CBR). This paper presents selected work from the history of "CBR in Medicine", clarifying that several systems already have been developed in the past, but the medical domain also still provides new problems and challenges, where a CBR-approach can be profitably applied. Currently, we are developing a CBR-based component, able to retrieve medical documents in written text form and to convert them into new patterns like a DICOM Structured Report. The component will be integrated in our existing transparent gateway for distributed data access to enable the collaboration of several health care organizations.

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“…A DF() is constructed using the extracted features. There can be one or more DF() defined over a specific set of images, such as similarity based on their histograms [2], number of object occurrences and object placement [4], etc.…”

Section: Introductionmentioning

confidence: 99%

Extending relational databases to support content-based retrieval of medical images

Araujo

Traina

et al.

Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)

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This paper shows how to support images in a relational database, so it can fulfill the requirements to be used as the storage mechanism of a PACS. This support includes the ability to answer similarity queries based on the image content, providing fast image retrieval based on indexing structures. The main concept allowing this support is the definition of distance functions based on features, which are extracted from the images as they are stored in the database. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translate them to standard SQL, allowing to take advantage of any relational database server. We describe experiments made with a prototype implemented using these concepts, which allowed answering queries up to 20 times faster than using only existing relational servers.

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CT scans with neurocysticercosis in epileptics patients: a computer-based method for detection and quantification

Cited by 5 publications

References 15 publications

A framework for radiological assistant systems

A framework for radiological assistant systems

Integrating CBR into the health care organization

Extending relational databases to support content-based retrieval of medical images

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