Iconic modelling for the progressive transmission of neurological images: segmentation

Author

Salous, M.N. ; Pycock, D. ; Cruickshank, G.S.

Author_Institution

Sch. of Electron. & Electr. Eng., Birmingham Univ., UK

Volume

1

fYear

2000

fDate

2000

Firstpage

512

Abstract

We present a knowledge-based segmentation scheme for use in the transmission of high resolution medical images. Segmentation is used to generate a compact iconic model which can be transmitted rapidly to provide an early indication of image structure. The boundaries of the iconic image are modelled using a novel superelliptic shape-tree. Each part of the iconic image is progressively updated, using a set of rules that take into account viewing requirements, to provide all informative image build-up, in a timely manner. We show that a simple knowledge base is adequate to describe a wide range of variation in MR and CT images, and achieve a segmentation that can be modelled to provide the iconic image

Keywords

biomedical MRI; computerised tomography; image segmentation; inference mechanisms; knowledge based systems; medical image processing; neurophysiology; CT images; MR images; iconic image; image segmentation; inference engine; knowledge-based systems; medical images; neurological images; Bandwidth; Biomedical imaging; Computed tomography; Hospitals; IP networks; Image communication; Image resolution; Image segmentation; Internet telephony; Medical diagnostic imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

Conference_Location

Barcelona

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.905388

Filename

905388