In this work we compare 3D Gyrification Index and our recently proposed area-independent curvature-based
surface measures  for the in-vivo quantification of brain surface folding in clinically acquired neonatal MR
image data. A meaningful comparison of gyrification across brains of different sizes and their subregions will only
be possible through the quantification of folding with measures that are independent of the area of the region of
analysis. This work uses a 3D implementation of the classical Gyrification Index, a 2D measure that quantifies
folding based on the ratio of the inner and outer contours of the brain and which has been used to study gyral
patterns in adults with schizophrenia, among other conditions. The new surface curvature-based measures and
the 3D Gyrification Index were calculated on twelve premature infants (age 28-37 weeks) from which surfaces of
cerebrospinal fluid/gray matter (CSF/GM) interface and gray matter/white matter (GM/WM) interface were
extracted. Experimental results show that our measures better quantify folding on the CSF/GM interface than
Gyrification Index, and perform similarly on the GM/WM interface.
In this paper we describe the application of folding measures to tracking <i>in vivo</i> cortical brain development in premature neonatal brain anatomy. The outer gray matter and the gray-white matter interface surfaces were extracted from semi-interactively segmented high-resolution T1 MRI data. Nine curvature- and geometric descriptor-based folding measures were applied to six premature infants, aged 28-37 weeks, using a direct voxelwise iso-surface representation. We have shown that using such an approach it is feasible to extract meaningful surfaces of adequate quality from typical clinically acquired neonatal MRI data. We have shown that most of the folding measures, including a new proposed measure, are sensitive to changes in age and therefore applicable in developing a model that tracks development in premature infants. For the first time gyrification measures have been computed on the gray-white matter interface and on cases whose age is representative of a period of intense brain development.
In many cases three-dimensional anatomical and functional images (SPECT, PET, MRI, CT) ought to be combined to determine the precise nature and extent of lesions in many parts of the body. The images must be adequately aligned prior to any addition, substraction, or any other combination; registration can be done by experienced radiologists via visual inspection, mental reorientation and overlap of slices, or by an automated registration algorithm. To be useful clinically, the latter case requires validation. The human capacity to evaluate registration results visually is limited and the process is time consuming. This paper describes an algorithmic procedure that distinguishes between badly misregistered pairs and those likely to be clinically useful. Our algorithm used brain and/or skin/air contours and a function based on the principal axes of the contour volumes. The results of the present study indicate that the measure based on the combination of brain and skin contours and a principal-axis function is a good first step to reduce the number of badly registered images reaching the clinician.
Before a retrospective registration algorithm can be used routinely in the clinic, methods must be provided for distinguishing between registration solutions that are clinically satisfactory and those that are not. One approach is to rely on a human observer. Here, we present an algorithmic procedure for assessing quality that discriminates between badly misregistered pairs and those that are clinically useful.
Failure to align images accurately often is due to the optimization algorithms being trapped in local maxima or spurious global maxima of the mutual information function. Strategies contemplated to improve registration involve modifying the optimization scheme or the registration measure itself. We recently found that normalized mutual information (for 2D image registration) provides a larger capture range and that is more robust, with respect to the optimization parameters, than the non-normalized measure. In this paper we assessed the utility of a stochastic global optimization technique for image registration using normalized and non-normalized mutual information. By conducting large-scale studies with patient data in 2D, we established a success rate baseline with the local optimizer only. Formal proof has not yet been found that incorporating the global optimizer does not impair performance. However, experiments to date indicate that its inclusion leads to better (i.e., higher probability of correct convergence) overall performance. More over, studies now underway show good effectiveness of our approach in a variety of 3D cases.
Proc. SPIE. 3338, Medical Imaging 1998: Image Processing
KEYWORDS: Image fusion, Magnetic resonance imaging, Image processing, Image analysis, Image registration, Medical imaging, Information theory, Optimization (mathematics), Probability theory, Image information entropy
Previous image registration schemes based on mutual information use Shannon's entropy measure, and they have been successfully applied for mono- and multimodality registration. There are cases, however, where maximization of mutual information does not lead to the correct spatial alignment of a pair of images. Some failures are due to the presence of local or spurious global maxima. In this paper we explore whether the normalization of mutual information via the use of a weight based on the size of region of overlap, improves the rate of successful alignments by reducing the presence of suboptimal extrema. In addition, we examine the utility of a deterministic entropy measure. The results of the present study indicate that: (1) the normalized mutual information provides a larger capture range and is more robust, with respect to optimization parameters, than the non-normalized mutual information, and (2) the optimization of mutual information with the deterministic entropy measure takes, on average, fewer iterations than when using Shannon's entropy measure. We conclude that the normalized mutual information using the deterministic entropy measure is a faster and more robust function for registration than the traditional mutual information.