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Pregled bibliografske jedinice broj: 10557

Segmentation of CT Head Images


Lončarić, Sven; Ćosić, Dubravko; Dhawan, Atam P.
Segmentation of CT Head Images // Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy / Lemke, Heinz U. et all. (ur.).
Pariz, Francuska: Elsevier Science, 1996. str. 1012-1012 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 10557 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Segmentation of CT Head Images

Autori
Lončarić, Sven ; Ćosić, Dubravko ; Dhawan, Atam P.

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy / Lemke, Heinz U. et all. - : Elsevier Science, 1996, 1012-1012

Skup
International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy

Mjesto i datum
Pariz, Francuska, ??-??.06. 1996

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Sažetak
Segmentation of human head images obtained by computed tomography (CT) plays a central role in intelligent image analysis. A new method for CT head image segmentation is presented in this work. In particular, segmentation of human spontaneous intracerebral brain hemorrhage (ICH) is important for quantitative analysis of ICH The proposed procedure classifies each CT image pixel into one of the following regions: background, skull, brain, ICH , and edema. CT head image segmentation has shown to be a challenging task. Most regions are relatively well localized but there is a lot of ambiguity in the edema localization. The proposed method consists of two main phases. An unsupervised fuzzy clustering algorithm is used in the first phase to generate a number of spatially localized image regions having uniform brightness. The unsupervised fuzzy clustering algorithm used in this work is a combination of the fuzzy C-means algorithm and the fuzzy maximum likelihood estimation. The unsupervised algorithm is used because no prior knowledge about the number of clusters is available. In the second phase an image labeling algorithm is used to merge multiple clusters of similar properties into unique image regions. The backtracking tree search algorithm has been used to find solutions of the labeling problem. The algorithm assigns a label to each of the small regions resulting from the clustering phase. The label set consists of five elements: background, skull, brain tissue, hemorrhage, and edema. Constraints are imposed on the solution of the labeling algorithm using region neighborhood relations and region label relations. The proposed method has been tested on real CT head images and has shown satisfactory results.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Projekti:
036024

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Sven Lončarić (autor)


Citiraj ovu publikaciju:

Lončarić, Sven; Ćosić, Dubravko; Dhawan, Atam P.
Segmentation of CT Head Images // Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy / Lemke, Heinz U. et all. (ur.).
Pariz, Francuska: Elsevier Science, 1996. str. 1012-1012 (poster, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Lončarić, S., Ćosić, D. & Dhawan, A. (1996) Segmentation of CT Head Images. U: Lemke, H. (ur.)Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy.
@article{article, editor = {Lemke, H.}, year = {1996}, pages = {1012-1012}, keywords = {}, title = {Segmentation of CT Head Images}, keyword = {}, publisher = {Elsevier Science}, publisherplace = {Pariz, Francuska} }
@article{article, editor = {Lemke, H.}, year = {1996}, pages = {1012-1012}, keywords = {}, title = {Segmentation of CT Head Images}, keyword = {}, publisher = {Elsevier Science}, publisherplace = {Pariz, Francuska} }




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