Expectation Maximization Segmentation Research Articles

Automatic Detection of Tomato Leaf Deficiency using Soft Computing Technique

Indian Economy mostly depends on Agriculture. Agriculture and its value-added products will occupy considerable amount of gross domestic product (GDP) and provides employment to more than half of the country’s workforce. Among all the countries India is one of the world's largest producer of agriculture and horticulture crops. Among all the vegetables Tomato is one of the most important vegetable used to consume all over the world. Disease easily affect the tomato plant due to insects and nutrient deficiency. To detect nutrient deficiency using image segmentation and classification is the main focus of this paper. If detect nutrient deficiency in early stage then he yields increased and the disease caused due to lack of nutrient deficiency also reduced. In this paper k-means and Expectation maximization segmentation algorithms are used for segmentation and SVM classifier used for classification. Based on the results Expectation Maximation provide better result than K-means segmentation.

Segmentation and Classification of Breast Cancer Tumour

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation 
 Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

Evaluation and analysis of data driven in expectation maximization segmentation through various initialization techniques in medical images

The operation of partitioning an image into a collection of connected sets of pixels is known as image segmentation. Expectation Maximization (EM) segmentation requires appropriate initialization of tissue class mean and variance, as it is get stuck on particular possible area of intensity of the probability nature. Decisive initialization is a necessary exploratory process for the forthcoming convergence of the algorithm to best regional maximum of possibility task. Indiscriminate initialization is not dossier directed, deep from optimal, outcomes are not reproducible, do not take advantage of deep rooted patterns in the data or may be loaded on outliers. This paper evaluates the performance of EM segmentation with random initialization, histogram guided initialization and initialization with k-means with respect to computational complexity and root mean squared error of tissue class mean and variance, updated by EM, with manually estimated tissue class mean and variance as ground truth, on paramount plane, T1 contrast and Magnetic resonance (MR) images of Glioblastoma Multiframe (GBM) Edema complex. The random initialization and histogram guided initialization was experimented for k-means, from the clustered output of which; initial tissue class mean and variance for EM are derived. RMS error remains the same for EM initialized and histogram guided K-means. EM initialized with K-means which has histogram guided initialization converges fast than the random initialization K-means, but the computational time is more for the former initialization than the latter. The experimental evaluation of EM initialization schemes and Fuzzy Cluster Means (FCM) were performed in MATLAB. The efficacy of Fuzzy Cluster Means clustering was analyzed qualitatively on tumour-edema complex. FCM could identify only 3 classes including background in the MR specimens. FCM consider edema and certain parts of WM as a single tissue class. Similarly, FCM clubs GM, CSF and necrotic focus into tissue class and produced empty clusters.

Brain voxel classification in magnetic resonance images using niche differential evolution based Bayesian inference of variational mixture of Gaussians

Classification of brain voxels into gray matter, white matter, and cerebrospinal fluid (CSF) using magnetic resonance imaging (MRI) is pivotal for quantitative brain analyses. In spite of its computational effectiveness, the most commonly used statistical classification models are less capable of handling the intensity non-uniformity (INU) and partial volume effect (PVE), and hence may produce less accurate results. In this paper, we propose a novel approach, namely the VMG-NDE algorithm, to improve brain voxel classification in MRI images by considering all effects simultaneously. There are four planks in this algorithm, including (1) using variational mixture of Gaussians (VMG) model to characterize the variation of voxel values caused by PVE, (2) training a cohort of local VMG models on small data volumes extracted from the image to reduce the impact of INU, (3) employing the niche differential evolution (NDE) to infer each local VMG model, aiming to avoid falling into local optima, and (4) constructing a probabilistic brain atlas for each study and using it to incorporate the anatomy prior into the classification process. After training local VMG models, we classify each brain voxel using a linear combination of the predictions generated by all those models. This algorithm has been evaluated against the variational expectation-maximization based and genetic algorithm based segmentation algorithms and the segmentation routines in the widely used statistical parametric mapping (SPM) package, expectation-maximization segmentation (EMS) package and FSL package on both synthetic and clinical T1-weighted brain MRI studies. Our results suggest that the proposed algorithm can differentiate major brain tissue types more effectively and produce improved brain voxel classification accuracy.

Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study

The aim of this propensity-matched cohort study was to evaluate the impact of prenatal SSRI exposure and a history of maternal depression on neonatal brain volumes and white matter microstructure. SSRI-exposed neonates (n=27) were matched to children of mothers with no history of depression or SSRI use (n=54). Additionally, neonates of mothers with a history of depression, but no prenatal SSRI exposure (n=41), were matched to children of mothers with no history of depression or SSRI use (n=82). Structural magnetic resonance imaging and diffusion weighted imaging scans were acquired with a 3T Siemens Allegra scanner. Global tissue volumes were characterized using an automatic, atlas-moderated expectation maximization segmentation tool. Local differences in gray matter volumes were examined using deformation-based morphometry. Quantitative tractography was performed using an adaptation of the UNC-Utah NA-MIC DTI framework. SSRI-exposed neonates exhibited widespread changes in white matter microstructure compared to matched controls. Children exposed to a history of maternal depression but no SSRIs showed no significant differences in brain development compared to matched controls. No significant differences were found in global or regional tissue volumes. Additional research is needed to clarify whether SSRIs directly alter white matter development or whether this relationship is mediated by depressive symptoms during pregnancy.

Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography.

Lung segmentation is a fundamental step in many image analysis applications for lung diseases and abnormalities in thoracic computed tomography (CT). The authors have previously developed a lung segmentation method based on expectation-maximization (EM) analysis and morphological operations (EMM) for our computer-aided detection (CAD) system for pulmonary embolism (PE) in CT pulmonary angiography (CTPA). However, due to the large variations in pathology that may be present in thoracic CT images, it is difficult to extract the lung regions accurately, especially when the lung parenchyma contains extensive lung diseases. The purpose of this study is to develop a new method that can provide accurate lung segmentation, including those affected by lung diseases. An iterative neutrosophic lung segmentation (INLS) method was developed to improve the EMM segmentation utilizing the anatomic features of the ribs and lungs. The initial lung regions (ILRs) were extracted using our previously developed EMM method, in which the ribs were extracted using 3D hierarchical EM segmentation and the ribcage was constructed using morphological operations. Based on the anatomic features of ribs and lungs, the initial EMM segmentation was refined using INLS to obtain the final lung regions. In the INLS method, the anatomic features were mapped into a neutrosophic domain, and the neutrosophic operation was performed iteratively to refine the ILRs. With IRB approval, 5 and 58 CTPA scans were collected retrospectively and used as training and test sets, of which 2 and 34 cases had lung diseases, respectively. The lung regions manually outlined by an experienced thoracic radiologist were used as reference standard for performance evaluation of the automated lung segmentation. The percentage overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist) of the lung boundaries relative to the reference standard were used as performance metrics. The proposed method achieved larger POAs and smaller distance errors than the EMM method. For the 58 test cases, the average POA, Hdist, and AvgDist were improved from 85.4±18.4%, 22.6±29.4 mm, and 3.5±5.4 mm using EMM to 91.2±6.7%, 16.0±11.3 mm, and 2.5±1.0 mm using INLS, respectively. The improvements were statistically significant (p<0.05). To evaluate the accuracy of the INLS method in the identification of the lung boundaries affected by lung diseases, the authors separately analyzed the performance of the proposed method on the cases with versus without the lung diseases. The results showed that the cases without lung diseases were segmented more accurately than the cases with lung diseases by both the EMM and the INLS methods, but the INLS method achieved better performance than the EMM method in both cases. The new INLS method utilizing the anatomic features of the rib and lung significantly improved the accuracy of lung segmentation, especially for the cases affected by lung diseases. Improvement in lung segmentation will facilitate many image analysis tasks and CAD applications for lung diseases and abnormalities in thoracic CT, including automated PE detection.

Serum Ionized Calcium May Be Related to White Matter Lesion Volumes in Older Adults: A Pilot Study

White matter lesions have detrimental effects upon older adults, while serum calcium levels have been associated with elevated vascular risk and may be associated with these lesions. Depression, a serious mental disorder characterized by disturbances in calcium metabolism, may be an important contributor to any calcium-lesion relationship. This cross-sectional pilot study examined the association between serum ionized calcium (the physiologically active form of calcium) and white matter lesion volumes in a sample of depressed and non-depressed older adults (N = 42; 60 years and older). Serum ionized calcium was determined using an ion-selective electrode technique, while lesion volumes were estimated from magnetic resonance imaging using an automated expectation-maximization segmentation. A linear regression model, controlling for age and group (depression vs. comparison), showed a trend for a positive relationship between serum ionized calcium and white matter lesion volume (β = 4.34, SE = 2.27, t = 1.91, p = 0.063). Subsample analyses with depressed participants showed a significant positive relationship between higher ionic calcium and greater lesion volume (β = 6.41, SE = 2.53, t = 2.53, p = 0.018), but no association was found for non-depressed participants. Sex-specific subsample analyses showed a significant positive relationship between higher calcium and greater lesion volume in men only (β = 7.49, SE = 3.42, t = 2.19, p = 0.041). These preliminary results indicate that serum ionized calcium may be associated with white matter lesions in older adults, particularly among men and individuals with depression. Larger studies are needed to confirm these findings.

EM Segmentation: Automatic Tissue Class Intensity Initialization Using K-means

ABSTRACT Brain tissue segmentation is important in many medical image applications. We augmented the Expectation-Maximization segmentation algorithm in Slicer3 (www.spl.harvard.edu) . Currently, in the EM Segmenter module in Slicer3 user input is necessary to set tissue-class (Gray Matter, White Matter etc.) intensity values. Our contribution to the current pipeline is to automatically compute such values using k-means clustering. Our method can be applied to scans of varying intensity profiles and thus we obviate the need for a normalization step. We applied this pipeline on multiple datasets and our method was able to accurately classify tissue-classes. The implementation was done as a standalone utility in the Python programming language (www.python.org) and work is underway to incorporate it in the EM processing pipeline.

An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.

EM Segmentation of the Distal Femur and Proximal Tibia: A High-Throughput Approach to Anatomic Surface Generation

Fully automated segmentation of computed tomography (CT) images remains a challenge for musculoskeletal researchers. The surfaces generated from image segmentations are valuable for surgical evaluation and planning. Previously, we demonstrated the expectation maximization (EM) algorithm as a semi-automated method of bone segmentation from CT images. In this work, we improve upon the methodology of probability map generation and demonstrate extended applicability of EM-based segmentation to the distal femur and proximal tibia using 72 CT image sets. We also compare the resulting EM segmentations to manual tracings using overlap metrics and time. In the case of the distal femur, the resulting quality metrics had mean values of 0.91 and 0.95 for the Jaccard and Dice metrics, respectively. For the proximal tibia, the Jaccard and Dice metrics were 0.90 and 0.95, respectively. The EM segmentation method was 8 times faster than the average manual segmentation and required less than 4% of the human rater time. Overall, the EM algorithm offers reliable image segmentations with an increased efficiency in comparison to manual segmentation techniques.

3D Graph cut with new edge weights for cerebral white matter segmentation

Accurate and efficient automatic or semi-automatic brain image segmentation methods are of great interest to both scientific and clinical researchers of the human central neural system. Cerebral white matter segmentation in brain Magnetic Resonance Imaging (MRI) data becomes a challenging problem due to a combination of several factors like low contrast, presence of noise and imaging artifacts, partial volume effects, intrinsic tissue variation due to neurodevelopment and neuropathologies, and the highly convoluted geometry of the cortex. In this paper, we propose a new set of edge weights for the traditional graph cut algorithm ( Boykov and Jolly, 2001) to correctly segment the cerebral white matter from T1-weighted MRI sequence. In this algorithm, the edge weights of Boykov and Jolly (2001) are modified by comparing the probabilities of an individual voxel and its neighboring voxels to belong to different segmentation classes. A shape prior in form of a series of ellipses is used next to model the contours of the human skull in various 2D slices in the sequence. This shape constraint is imposed to prune the original graph constructed from the input to form a subgraph consisting of voxels within the skull contours. Our graph cut algorithm with new set of edge weights is applied to the above subgraph, thereby increasing the segmentation accuracy as well as decreasing the computation time. Average segmentation errors for the proposed algorithm, the graph cut algorithm ( Boykov and Jolly, 2001), and the Expectation Maximization Segmentation (EMS) algorithm Van Leemput et al., 2001 in terms of Dice coefficients are found to be (3.72 ± 1.12)%, (14.88 ± 1.69)%, and (11.95 ± 5.2)%, respectively.

New Expectation Maximization Segmentation Pipeline in Slicer 3

Many neuroanatomy studies rely on brain tissue segmentation in Magnetic Resonance images (MRI). The Expectation-Maximization (EM) theory offers a popular framework for this task. We studied the EM algorithm developed at the Surgical Planning Laboratory (SPL) at Harvard Medical School and implemented in the Slicer3 software. We observed that the segmentation lacks accuracy if the image exhibits some intensity inhomogeneity. Moreover the optimum parameters are challenging to estimate. This document aims at describing our solutions within the context of statistical modeling. Our contributions range from algorithm improvements to novel representations of the statistical distribution model. First we added a bias field correction module and exposed the most significant parameters. Second we proposed a new way to select the distribution of the tissues to be segmented. Finally we designed a set of interactive tools to make the segmentation process easier and more accurate. To validate the new segmentation pipeline, we performed our experiments on MRI data and a clinical expert evaluated our results.The source code developed for this work, i.e. the code of the MRIBiasFieldCorrection and EMSegment modules, is part of Slicer3 version 3.5 and can be downloaded by this command: svn co http://svn.slicer.org/Slicer3/trunk Slicer3

MR Image Segmentation Using a Power Transformation Approach

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.

Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

Medical imaging technologies have allowed for in vivo exploration and evaluation of the human musculoskeletal system. Three-dimensional bone models generated using image-segmentation techniques provide a means to optimize individualized orthopedic surgical procedures using engineering analyses. However, many of the current segmentation techniques are not clinically practical due to the required time and human intervention. As a proof of concept, we demonstrate the use of an expectation maximization (EM) algorithm to segment the hand phalanx bones, and hypothesize that this semi-automated technique will improve the efficiency while providing similar definitions as compared to a manual rater. Our results show a relative overlap of the proximal, middle, and distal phalanx bones of 0.83, 0.79, and 0.72 for the EM technique when compared to validated manual segmentations. The EM segmentations were also compared to 3D surface scans of the cadaveric specimens, which resulted in distance maps showing an average distance for the proximal, middle, and distal phalanx bones of 0.45, 0.46, and 0.51 mm, respectively. The EM segmentation improved on the segmentation speed of the manual techniques by a factor of eight. Overall, the manual segmentations had greater relative overlap metric values, which suggests that the manual segmentations are a better fit to the actual surface of the bone. As shown by the comparison to the bone surface scans, the EM technique provides a similar representation of the anatomic structure and offers an increase in efficiency that could help to reduce the time needed for defining anatomical structures from CT scans.

Patterns of White Matter Atrophy in Frontotemporal Lobar Degeneration

Structural magnetic resonance imaging (MRI) has been used to investigate the in vivo pathology of frontotemporal lobar degeneration. However, few neuroimaging studies have focused on white matter (WM) alterations in this disease. To use volumetric MRI techniques to identify the patterns of WM atrophy in vivo in 2 clinical variants of frontotemporal lobar degeneration-frontotemporal dementia (FTD) and semantic dementia-and to compare the patterns of WM atrophy with those of gray matter (GM) atrophy in these diseases. Structural MRIs were obtained from patients with FTD (n = 12) and semantic dementia (n = 13) and in cognitively healthy age-matched controls (n = 24). Regional GM and WM were classified automatically from high-resolution T1-, T2-, and proton density-weighted MRIs with Expectation-Maximization Segmentation and compared between the groups using a multivariate analysis of covariance model that included age and WM lesion volumes as covariates. Patients with FTD had frontal WM atrophy and frontal, parietal, and temporal GM atrophy compared with controls, who had none. Patients with semantic dementia had temporal WM and GM atrophy and patients with FTD had frontal GM atrophy. Adding temporal WM volume to temporal GM volume significantly improved the discrimination between semantic dementia and FTD. These results show that patients with frontotemporal lobar degeneration who are in relatively early stages of the disease (Clinical Dementia Rating score, 1.0-1.2) have WM atrophy that largely parallels the pattern of GM atrophy typically associated with these disorders.

Aggression and Quantitative MRI Measures of Caudate in Patients With Chronic Schizophrenia or Schizoaffective Disorder

Caudate dysfunction is implicated in schizophrenia. However, little is known about the relationship between aggression and caudate volumes. Forty-nine patients received magnetic resonance imaging scanning in a double-blind treatment study in which aggression was measured. Caudate volumes were computed using a semiautomated method. The authors measured aggression with the Overt Aggression Scale and the Positive and Negative Syndrome Scale. Larger caudate volumes were associated with greater levels of aggression. The relationship between aggression and caudate volumes may be related to the iatrogenic effects of long-term treatment with typical antipsychotic agents or to a direct effect of schizophrenic processes on the caudate.

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the “ground truth” as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.

IC-P-017: Different patterns of gray and white matter atrophy in Alzheimer’s disease and subtypes of frontotemproal lobar dementia

Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) are neurodegenerative diseases characterized by the progressive loss of cerebral tissue. Although structural magnetic resonance imaging (MRI) has been used extensively to investigate AD, and to a lesser extent FTLD, few studies have distinguished between the patterns of gray matter (GM) and white matter (WM) atrophy in these diseases. To identify the pattern of WM atrophy in two subtypes of FTLD–frontotemporal dementia (FTD) and semantic dementia (SD) and in AD relative to normal controls (CN). To compare the patterns of WM and GM atrophy in FTD, SD, and AD. Twelve FTD, 13 SD, 15 AD patients, and 24 cognitively normal subjects (CN) were studied with volumetric MRI. Regional GM and WM in each group were classified automatically from high resolution T1– and T2–weighted MRI using Expectation–Maximization Segmentation (EMS). Data were analyzed with analyses of covariance, with age and the white matter lesion volumes as covariates. Figure 1 shows the Z–scores of GM and WM volumes by lobe for each patient group. Relative to CN, AD patients had significant GM atrophy in the parietal and temporal lobes (p ⩽ 0.001). Although there was a trend for WM atrophy in the parietal lobe, there were no significant overall WM volume differences between AD and CN. FTD patients had significant GM and WM atrophy in the frontal lobe relative to CN (p ⩽ 0.001). There were trends of GM atrophy in the parietal and temporal lobes, but no WM atrophy in these regions. Compared to CN, SD patients had significant GM atrophy in both temporal and frontal lobes (p ⩽ 0.001), but significant WM atrophy only in the temporal lobe (p < 0.0001). These results suggest that different types of dementias are associated with different patterns of WM atrophy and that the pattern of WM atrophy differs by lobe. Whether WM atrophy in these diseases indicates general neuritic dystrophy, involving both neuron cell bodies and axons, or Wallerian degeneration remains to be explored.

452. Caudate and ventricular volume in unmedicated schizotypal personality disorder

A growing appreciation has emerged for the role of the basal ganglia in cognitive as well as in motor functioning. Anatomically, both motor and “cognitive” circuits exist that connect the frontal lobe to subcortical structures. Three such circuits originate in prefrontal regions believed important for higher cognitive functions, and topographically innervate the head of the caudate nucleus and nucleus accumbens forming starting and ending points for prefrontal “cognitive” cortical-basal ganglia circuits. Pathology in any of the core components of these circuits, such as in the caudate nucleus, may result in similar neurobehavioral syndromes. Neuroleptic medication, however, affects the size of the caudate nucleus, though reduced caudate volume reported in first-episode schizophrenia supports intrinsic pathology. For this reason, Schizotypal Personality Disorder (SPD) subjects offer an ideal group for the measurement of the caudate as they are genetically related to schizophrenia but do not require neuroleptic treatment because of their less severe symptoms. We have thus chosen to measure the caudate nucleus, and the contiguous lateral ventricles, in 14 right handed male SPDs, who have had no prior neuroleptic exposure, and 15 normal controls (NCLs), who were age, parental SES, handedness and sex matched to the SPD subjects. MRI scans were obtained on a 1.5 Tesla magnet. For the measurement of specific regions of interest higher spatial resolution SPGR images (1.5 × .9375 × .9375 mm voxels) were used. For whole brain measurements, used to correct for head size, a spin echo double echo MR sequence with 3 mm axial contiguous slices was obtained; then reformatted and co-registered to 1.5 mm SPGR coronal obtained images; the resulting image was then segmented using Wells’ expectation-maximization segmentation protocol. Repeated measures ANCOVA (with ICC and age as covariates) yielded a significant main effect for diagnosis (F = 6.37, df = 1,27, p < 0.02). Follow-up t-tests showed that right, left and total absolute caudate volume was smaller in SPD than in NCLs (4.77 v. 4.14 ml, p < 0.05; 4.68 v. 4.07 ml, p = 0.13; 9.45 v. 8.21 ml, p < 0.01). In a subsample of this group (7 SPD and 9 NCL subjects), we have measured that portion of the lateral ventricles contiguous to the caudate nuclei. Preliminary analysis revealed that the right and left contiguous ventricles in SPD subjects were not as large as in normal controls (4.2 vs. 6.1 ml, p < 0.2; 4.2 vs. 6.7 ml, p = 0.04), suggesting that smaller caudate size in SPD does not appear to be associated with enlarged lateral ventricles. These data are consistent with reduced caudate volume in first-episode schizophrenia studies and further suggest that there may be intrinsic pathology in the caudate nucleus in the schizophrenia spectrum population of SPD.