New hybrid stochastic-deterministic technique for fast registration of dermatological images.
Digital image processing in the medical field has become very popular in recent years owing to the significant advantages it offers over conventional techniques of visual or analogue image analysis. One of the most significant aspects in medical image processing has been that of image registration, which deals with the task of registering two images taken under different conditions. Image registration is considered an important issue in the field of dermatology, as pictures of a lesion taken in different periods need to be compared and quantitatively analysed. A hybrid image registration scheme was developed and evaluated for dermatological applications. The method splits the parameter estimation problem into two, with a combination of deterministic and iterative estimation techniques. The scaling and rotation parameters are estimated using a cross-correlation of image invariant image descriptors algorithm, whereas the two translation parameters are estimated with a non-parametric similarity criterion and a hill-climbing optimisation scheme. The efficacy of the method has been validated for the registration and comparison of malignant melanoma images. Determination of rotation and scaling parameters was performed using the log-polar transformation technique, which proved to be very accurate, even when high rotation and scaling values were imposed. Deviations for the rotation parameter estimations were less than 0.5%, whereas, for the scaling factor, differences were on average less than 2.5%, with a maximum difference estimated to be 4.5%. Translation parameter estimation was performed using integer similarity measures namely the stochastic sign change, the deterministic sign change (DSC) and the window value range, the performance of which has been assessed and, in all cases, was found to be highly effective. A novel hill-climbing optimisation algorithm has been proposed and, in combination with the DSC similarity criterion, was evaluated and proved to successfully estimate translation parameters. Thus the proposed hybrid registration technique can successfully estimate problem parameters in a time-efficient manner.
Pavlopoulos
S A
SA
Biomedical Engineering Laboratory, School of Electrical & Computer Engineering, National Technical University of Athens, Greece. spav@biomed.ntua.gr
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000175:diagnosis*; D009367:Neoplasm Staging; D012878:Skin Neoplasms / Q000175:diagnosis*; D013269:Stochastic Processes