  Children diagnosed with a scoliosis pathology are exposed during their follow
up to ionic radiations in each X-rays diagnosis. This exposure can have
negative effects on the patient's health and cause diseases in the adult age.
In order to reduce X-rays scanning, recent systems provide diagnosis of
scoliosis patients using solely RGB images. The output of such systems is a set
of augmented images and scoliosis related angles. These angles, however,
confuse the physicians due to their large number. Moreover, the lack of X-rays
scans makes it impossible for the physician to compare RGB and X-rays images,
and decide whether to reduce X-rays exposure or not. In this work, we exploit
both RGB images of scoliosis captured during clinical diagnosis, and X-rays
hard copies provided by patients in order to register both images and give a
rich comparison of diagnoses. The work consists in, first, establishing the
monomodal (RGB topography of the back) and multimodal (RGB and Xrays) image
database, then registering images based on patient landmarks, and finally
blending registered images for a visual analysis and follow up by the
physician. The proposed registration is based on a rigid transformation that
preserves the topology of the patient's back. Parameters of the rigid
transformation are estimated using a proposed angle minimization of Cervical
vertebra 7, and Posterior Superior Iliac Spine landmarks of a source and target
diagnoses. Experiments conducted on the constructed database show a better
monomodal and multimodal registration using our proposed method compared to
registration using an Equation System Solving based registration.
