Medical image registration using TSallis Entropy in Statistical Parametric Mapping (SPM).

The superposition of medical images, technically known as co-registration, can take a major role in determining the topographic and morphological changes in functional diagnostic and therapeutic purposes. This paper describes a study focused on to find an alternative cost function method for medical images co-registration through the study of performance and robustness of the TSallis Entropy in Statistical Parametric Mapping package (SPM). Images of Magnetic Resonance (MR) and Single Photon Emission Computed Tomography (SPECT) of 3 patients morphologically normal were used for the construction of anatomic phantoms containing predetermined geometric variations. The simulated images were co-registered with the original images using traditional techniques and the proposed method. The comparative analysis of the Root Mean Square (RMS) error showed that the Tsallis Entropy was more efficient in the intramodality alignment, while the Shannon Entropy in the intermodality one; revealing therefore the importance of the implementation of the Tsallis Entropy in SPM for applications in neurology and neuropsychiatric evaluation.

Automated grading of left ventricular segmental wall motion by an artificial neural network using color kinesis images.

The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R2 = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.