In this paper we present a gradient descent flow based on a novel energy functional that is capable of producing
robust and accurate segmentations of medical images. This flow is a hybridization of local geodesic active
contours and more global region-based active contours. The combination of these two methods allows curves
deforming under this energy to find only significant local minima and delineate object borders despite noise,
poor edge information, and heterogeneous intensity profiles. To accomplish this, we construct a cost function
that is evaluated along the evolving curve. In this cost, the value at each point on the curve is based on the
analysis of interior and exterior means in a local neighborhood around that point. We also demonstrate a novel
mathematical derivation used to implement this and other similar flows. Results for this algorithm are compared
to standard techniques using medical and synthetic images to demonstrate the proposed method's robustness
and accuracy as compared to both edge-based and region-based alone.
The striatum is the input component of the basal ganglia from the cerebral cortex. It includes the caudate, putamen,
and nucleus accumbens. Thus, the striatum is an important component in limbic frontal-subcortical circuitry and is
believed to be relevant both for reward-guided behaviors and for the expression of psychosis. The dorsal striatum is
composed of the caudate and putamen, both of which are further subdivided into pre- and post-commissural components.
The ventral striatum (VS) is primarily composed of the nucleus accumbens. The striatum can be functionally divided
into three broad regions: 1) a limbic; 2) a cognitive and 3) a sensor-motor region. The approximate corresponding
anatomic subregions for these 3 functional regions are: 1) the VS; 2) the pre/post-commissural caudate and the pre-commissural
putamen and 3) the post-commissural putamen.
We believe assessing these subregions, separately, in disorders with limbic and cognitive impairment such as
schizophrenia may yield more informative group differences in comparison with normal controls than prior parcellation
strategies of the striatum such as assessing the caudate and putamen. The manual parcellation of the striatum into these
subregions is currently defined using certain landmark points and geometric rules. Since identification of these areas is
important to clinical research, a reliable and fast parcellation technique is required.
Currently, only full manual parcellation using editing software is available; however, this technique is extremely
time intensive. Previous work has shown successful application of heuristic rules into a semi-automatic platform1. We
present here a semi-automatic algorithm which implements the rules currently used for manual parcellation of the
striatum, but requires minimal user input and significantly reduces the time required for parcellation.
Structural, functional, and clinical studies in schizophrenia have, for several decades, consistently implicated
dysfunction of the prefrontal cortex in the etiology of the disease. Functional and structural imaging studies,
combined with clinical, psychometric, and genetic analyses in schizophrenia have confirmed the key roles
played by the prefrontal cortex and closely linked "prefrontal system" structures such as the striatum, amygdala,
mediodorsal thalamus, substantia nigra-ventral tegmental area, and anterior cingulate cortices. The nodal
structure of the prefrontal system circuit is the dorsal lateral prefrontal cortex (DLPFC), or Brodmann area 46,
which also appears to be the most commonly studied and cited brain area with respect to schizophrenia.1, 2, 3, 4
In 1986, Weinberger et. al. tied cerebral blood flow in the DLPFC to schizophrenia.1 In 2001, Perlstein et. al.
demonstrated that DLPFC activation is essential for working memory tasks commonly deficient in schizophrenia.
2 More recently, groups have linked morphological changes due to gene deletion and increased DLPFC
glutamate concentration to schizophrenia.3, 4
Despite the experimental and clinical focus on the DLPFC in structural and functional imaging, the variability
of the location of this area, differences in opinion on exactly what constitutes DLPFC, and inherent difficulties in
segmenting this highly convoluted cortical region have contributed to a lack of widely used standards for manual
or semi-automated segmentation programs.
Given these implications, we developed a semi-automatic tool to segment the DLPFC from brain MRI scans
in a reproducible way to conduct further morphological and statistical studies. The segmenter is based on
expert neuroanatomist rules (Fallon-Kindermann rules), inspired by cytoarchitectonic data and reconstructions
presented by Rajkowska and Goldman-Rakic.5 It is semi-automated to provide essential user interactivity. We
present our results and provide details on our DLPFC open-source tool.
In this paper we propose a principled approach for shape comparison. Given two surfaces, one to one correspondences are determined using the Laplace equation. The distance between corresponding points is then used to define both global and local dissimilarity statistics between the surfaces. This technique provides a powerful method to compare shapes both locally and globally for the purpose of segmentation, registration or shape analysis. For improved accuracy, we propose a Boundary Element Method. Our approach is applicable to datasets of any dimension and offers subpixel resolution. We illustrate the usefulness of the technique for validation of segmentation, by defining global dissimilarity statistics and visualizing errors locally on color-coded surfaces. We also show how our technique can be applied to multiple shapes comparison.