Trainable rule-based network for irradiation field recognition in Agfa's ADC system

Piet Dewaele; Michael Ibison; Pieter Vuylsteke

doi:10.1117/12.237836

11 April 1996 Trainable rule-based network for irradiation field recognition in Agfa's ADC system

Piet Dewaele, Michael Ibison, Pieter Vuylsteke

Proceedings Volume 2708, Medical Imaging 1996: Physics of Medical Imaging; (1996) https://doi.org/10.1117/12.237836
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

The irradiation field is the diagnostic region of radiograph which has been exposed directly to x rays and has not been shielded from the source by x-ray opaque material. Such material may have been placed to shield vulnerable regions of the patient from unnecessary exposure, and also to partition the radiograph into sub-images, thereby permitting multiple exposures on the same plate. In this paper we describe an image analysis method to automatically detect and locate the irradiation field of a digital radiograph. The aim is to prepare the radiographic image for contrast-enhancement processing which is driven only by exposure data from the diagnostically useful part of the radiograph. The algorithm is broadly edge-based, and comprises four stages: (1) edge detection, culminating in a set of straight-line edge segments as non-iconic data structures; (2) clustering (where possible) these segments into longer lines in accordance with colinearity constraints and the like; (3) model matching by a trainable rule- based network to identify irradiation field boundaries; (4) masking, which acts upon the input image to blank out the regions not in the irradiation field. The network has been trained on a database of 2100 images, and has been tested in clinical use, delivering an accuracy for recognition of the irradiation field of better than 99%.

Citation Download Citation

Piet Dewaele, Michael Ibison, and Pieter Vuylsteke "Trainable rule-based network for irradiation field recognition in Agfa's ADC system", Proc. SPIE 2708, Medical Imaging 1996: Physics of Medical Imaging, (11 April 1996); https://doi.org/10.1117/12.237836

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