Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Segmentation of CT Head Images (CROSBI ID 465697)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

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.). Elsevier, 1996. str. 1012-1012-x

Podaci o odgovornosti

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

engleski

Segmentation of CT Head Images

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.

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o prilogu

1012-1012-x.

1996.

nije evidentirano

objavljeno

Podaci o matičnoj publikaciji

Proceedings of the International Symposium on Computer and Communication Systems for Image Guided Diagnosis and Therapy

Lemke, Heinz U. et all.

Elsevier

Podaci o skupu

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

poster

01.01.1996-01.01.1996

Pariz, Francuska

Povezanost rada

Elektrotehnika