Title :
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
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;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.905388
