Fuzzy Image Enhancement and Associative Feature Matching in Radiotherapy

The Electronic Portal Imaging Device has become an important tool for the clinician to verify the shape and the location of the therapy beam with respect to the patient’s anatomy [13]. Electronic Portal Imaging Devices use the high-energy treatment beam to project the body interior of the patient onto a fluorescent screen that is scanned by a camera. After on-line digitization, the images are available on a computer screen next to console of the accelerator. Normally, a visual comparison of the real patient position related to the beam with the planned treatment field is performed. This treatment field is defined during diagnostics and treatment planning. For this purpose, a treatment simulation takes place as a result of which a simulator image (SI) is captured. Because of the imaging physics the unprocessed Electronic Portal Images (EPI) are very poor in quality compared with the SI that is usually an X-ray image from CT. Together with the increasing importance of 3-D treatment planning, CT slices and their projections (Digitally Reconstructed Radiographs) or CT scout view images are used. The conventional EPI allows only a rough verification of patient position relative to bony structures. At the state of the art conventional enhancement techniques can be applied to EPIs that give some improvement for further visual analysis after the treatment (off-line). This paper presents an approach that combines an Associative Restoration algorithm with an Fuzzy Image Enhancement technique to reach a new quality. Fig. 1 demonstrates the overall system. The main idea of the Associative Restoration is the merger of the EPI with the SI to generate a much better in-treatment image than obtained by simple enhancement and to allow a more reliable feature matching. Firstly, the images are enhanced by the Fuzzy Image Enhancement as a result of which the visibility of structures like bones is improved. This is important also for the following alignment of corresponding structures in the images. A specially structured Artificial Neural Network that we call Modified Associative Memory is trained by the enhanced SI.