Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images

Signal Normalization among Multiple Optical Coherence Tomography Devices

Optical coherence tomography (OCT) has become a clinical standard in ophthalmology because it has the ability to provide in vivo cross-sectional images of ocular tissues with microscopic resolution in a non-contact and non-invasive manner. More and more manufacturers are getting involved in the race of instrument design and the development of the spectral-domain OCT (SD-OCT). Various light sources, optical designs, and image acquisition settings were employed by different manufacturers to stand out among competitors. This provides a wide variety of options in terms of scanning protocol, image processing, and presentation. However, the diversity also reflects in the variability in the OCT signal characteristics. The variability of OCT signal characteristics not only results in systematic differences in OCT measurement data, such as the retinal nerve fiber layer (RNFL) thickness and total retinal thickness, but also induces discrepancies in OCT image appearance. Those differences cause serious clinical challenges when comparing OCT images from different OCT devices, or recruiting multiple OCT devices in one study. To solve this problem, a novel signal normalization method was developed in this dissertation. The signal normalization was developed in a stepwise fashion to resolve all factors contributing to the systematic differences among various OCT devices, including axial sampling density, the amount of speckle noise, intensity dynamic range, and image quality. Quantitative analyses and qualitative assessments were conducted to evaluate the proposed signal normalization method. For the quantitative analyses, engineering and clinical validations were performed via measuring the absolute differences in A-scan profile intensity and comparing the systematic RNFL thickness differences before and after signal normalization. For the qualitative assessment, subjective evaluation of the similarity of OCT image appearance through a questionnaire was performed. Statistically significant reduction in both the absolute difference in A-scan profile and the systematic differences among SD-OCT devices were observed after signal normalization. Statistically significant improvements of image similarity between OCT image pairs were also found after the processing. With the proposed signal normalization method, quantitative analysis as well as qualitative assessment among OCT devices will become directly comparable, which would broaden the use of OCT technology in both clinical and research applications