Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia.

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia.

Abnormalities in fronto-striatal connectivity within language networks relate to differences in grey-matter heterogeneity in Asperger syndrome.

Asperger syndrome (AS) is an Autism Spectrum Disorder (ASD) characterised by qualitative impairment in the development of emotional and social skills with relative preservation of general intellectual abilities, including verbal language. People with AS may nevertheless show atypical language, including rate and frequency of speech production. We previously observed that abnormalities in grey matter homogeneity (measured with texture analysis of structural MR images) in AS individuals when compared with controls are also correlated with the volume of caudate nucleus. Here, we tested a prediction that these distributed abnormalities in grey matter compromise the functional integrity of brain networks supporting verbal communication skills. We therefore measured the functional connectivity between caudate nucleus and cortex during a functional neuroimaging study of language generation (verbal fluency), applying psycho-physiological interaction (PPI) methods to test specifically for differences attributable to grey matter heterogeneity in AS participants. Furthermore, we used dynamic causal modelling (DCM) to characterise the causal directionality of these differences in interregional connectivity during word production. Our results revealed a diagnosis-dependent influence of grey matter heterogeneity on the functional connectivity of the caudate nuclei with right insula/inferior frontal gyrus and anterior cingulate, respectively with the left superior frontal gyrus and right precuneus. Moreover, causal modelling of interactions between inferior frontal gyri, caudate and precuneus, revealed a reliance on bottom-up (stimulus-driven) connections in AS participants that contrasted with a dominance of top-down (cognitive control) connections from prefrontal cortex observed in control participants. These results provide detailed support for previously hypothesised central disconnectivity in ASD and specify discrete brain network targets for diagnosis and therapy in ASD.

Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage.

The aim was to undertake an initial study of the relationship between texture features in computed tomography (CT) images of non-small cell lung cancer (NSCLC) and tumour glucose metabolism and stage. This retrospective pilot study comprised 17 patients with 18 pathologically confirmed NSCLC. Non-contrast-enhanced CT images of the primary pulmonary lesions underwent texture analysis in 2 stages as follows: (a) image filtration using Laplacian of Gaussian filter to differentially highlight fine to coarse textures, followed by (b) texture quantification using mean grey intensity (MGI), entropy (E) and uniformity (U) parameters. Texture parameters were compared with tumour fluorodeoxyglucose (FDG) uptake (standardised uptake value (SUV)) and stage as determined by the clinical report of the CT and FDG-positron emission tomography imaging. Tumour SUVs ranged between 2.8 and 10.4. The number of NSCLC with tumour stages I, II, III and IV were 4, 4, 4 and 6, respectively. Coarse texture features correlated with tumour SUV (E: r = 0.51, p = 0.03; U: r = -0.52, p = 0.03), whereas fine texture features correlated with tumour stage (MGI: rs = 0.71, p = 0.001; E: rs = 0.55, p = 0.02; U: rs = -0.49, p = 0.04). Fine texture predicted tumour stage with a kappa of 0.7, demonstrating 100% sensitivity and 87.5% specificity for detecting tumours above stage II ( p = 0.0001). This study provides initial evidence for a relationship between texture features in NSCLC on non-contrast-enhanced CT and tumour metabolism and stage. Texture analysis warrants further investigation as a potential method for obtaining prognostic information for patients with NSCLC undergoing CT.

Three-dimensional textural analysis of brain images reveals distributed grey-matter abnormalities in schizophrenia.

OBJECTIVES: Three-dimensional (3-D) selective- and relative-scale texture analysis (TA) was applied to structural magnetic resonance (MR) brain images to quantify the presence of grey-matter (GM) and white-matter (WM) textural abnormalities associated with schizophrenia. MATERIALS AND METHODS: Brain TA comprised volume filtration using the Laplacian of Gaussian filter to highlight fine, medium and coarse textures within GM and WM, followed by texture quantification. Relative TA (e.g. ratio of fine to medium) was also computed. T1-weighted MR whole-brain images from 32 participants with diagnosis of schizophrenia (n = 10) and healthy controls (n = 22) were examined. Five patients possessed marker alleles (SZ8) associated with schizophrenia on chromosome 8 in the pericentriolar material 1 gene while the remaining five had not inherited any of the alleles (SZ0). RESULTS: Filtered fine GM texture (mean grey-level intensity; MGI) most significantly differentiated schizophrenic patients from controls (P = 0.0058; area under the receiver-operating characteristic curve = 0.809, sensitivity = 90%, specificity = 70%). WM measurements did not distinguish the two groups. Filtered GM and WM textures (MGI) correlated with total GM and WM volume respectively. Medium-to-coarse GM entropy distinguished SZ0 from controls (P = 0.0069) while measures from SZ8 were intermediate between the two. CONCLUSIONS: 3-D TA of brain MR enables detection of subtle distributed morphological features associated with schizophrenia, determined partly by susceptibility genes.

Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival.

PURPOSE: To assess the utility of texture analysis of liver computed tomographic (CT) images by determining the effect of acquisition parameters on texture and by comparing the abilities of texture analysis and hepatic perfusion CT to help predict survival for patients with colorectal cancer. MATERIALS AND METHODS: The study comprised a phantom test and a clinical evaluation of 48 patients with colorectal cancer who had consented to retrospective analysis of hepatic perfusion CT data acquired during a research study approved by the institutional review board. Both components involved texture analysis to quantify the relative contribution of CT features between 2 and 12 pixels wide to overall image brightness and uniformity. The effect of acquisition factors on texture was assessed on CT images of a cylindric phantom filled with water obtained by using tube currents between 100 and 250 mAs and voltages between 80 and 140 kVp. Texture on apparently normal portal phase CT images of the liver and hepatic perfusion parameters were related to patient survival by using Kaplan-Meier survival analysis. RESULTS: A texture parameter that compared the uniformity of distribution of CT image features 10 and 12 pixels wide exhibited the least variability with CT acquisition parameters (maximum coefficient of variation, 2.6%) and was the best predictor of patient survival (P < .005). There was no significant association between survival and hepatic perfusion parameters. CONCLUSION: The study provides preliminary evidence that analysis of liver texture on portal phase CT images is potentially a superior predictor of survival for patients with colorectal cancer than CT perfusion imaging. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/2502071879/DC1.

Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver.

OBJECTIVES: To determine whether texture analysis of non-contrast enhanced computed tomography (CT) images in apparently disease-free areas of the liver is altered by the presence of extra- and intra-hepatic malignancy in colorectal cancer patients. MATERIALS AND METHODS: Hepatic attenuation and texture were assessed from non-contrast enhanced CT in three groups of colorectal cancer patients: (A) 15 controls with no malignancy; (B) nine patients with extra-hepatic malignancy but no liver involvement; (C) eight patients with hepatic metastases. Regions of interest were manually constructed only over apparently normal areas of liver tissue excluding major blood vessels and areas of intra-hepatic fat, which may otherwise alter CT texture irrespective of the presence of malignancy. Texture was analysed on unfiltered images and following band-pass image filtration to highlight image features at different spatial frequencies (fine: 2 pixels/1.68 mm in width, medium: 6 pixels/5.04 mm and coarse: 12 pixels/10.08 mm). The relative contributions made to the image by features at two different spatial frequencies were expressed as filter ratios (fine/medium, fine/coarse and medium/coarse). Texture was quantified as mean grey-level intensity, entropy and uniformity. RESULTS: Texture was not altered on unfiltered images whereas relative texture analysis following image filtration identified differences in fine to medium texture ratios in apparently disease-free areas of the liver in patients with hepatic metastases as compared to patients with no tumour (entropy, p=0.0257) and patients with extra-hepatic disease (uniformity, p=0.0143). CONCLUSIONS: Relative texture analysis of unenhanced hepatic CT can reveal changes in apparently disease-free areas of the liver that have previously required more complex perfusion measurements for detection.

Performance assessment of the modified-hybrid optical neural network filter.

We present in detail the recorded results of the modified-hybrid optical neural network (M-HONN) filter during a full series of tests to examine its robustness and overall performance for object recognition tasks. We test the M-HONN filter for its detectability and peak sharpness with within-class distortion of the input object, its discrimination ability between an in-class and out-of-class object, and its performance with cluttered images of the true-class object. The M-HONN filter is found to exhibit good detectability, an ability to maintain its correlation-peak sharpness throughout the recorded tests, good discrimination ability, and an ability to detect the true-class object within cluttered input images. Additionally we observe the M-HONN filter's performance within the tests in comparison with the constrained-hybrid optical neural network filter for the first three series of tests and the synthetic discriminant function-maximum average correlation height filter for the fourth set of tests.

Three-dimensional selective-scale texture analysis of computed tomography pulmonary angiograms.

OBJECTIVES: This feasibility study aims to develop 3-dimensional (3D) selective-scale texture analysis of computed tomography pulmonary angiography to identify texture correlates for ventilated and vascular lung for visual and quantitative assessment of pulmonary disorders with altered vasculature. MATERIALS AND METHODS: Computed tomography pulmonary angiography examinations of 8 patients were considered in this study; 3 had normal lungs, 3 had pulmonary embolism (PE1, PE2, and PE3), 1 had only emphysema (PEmp), whereas the final patient had both emphysema and embolism (PEE). Before texture analysis, an initial automated segmentation procedure to include only the lung parenchyma and generation of isometric volume were carried out. From this segmented volume, ventilated lung and pulmonary vessels were separately selected. Texture analysis comprised 2 stages: 1) volume filtration using 3D Laplacian of Gaussian filter to highlight fine and coarse textures within ventilated and vascular lung, followed by 2) quantification of texture using mean gray-level intensity, entropy and uniformity both globally and at 3 anatomic sections of the lung, ie, anterior, middle, and posterior. Quantification of texture was also performed on the unfiltered computed tomography lung dataset. Volume rendering and image fusion of ventilated and vascular lung texture were employed for visualization. RESULTS: For fine texture quantified as mean gray-level intensity in ventilated lung, a postural gradient compatible with known pulmonary physiology was demonstrated and texture was different in emphysematous lung (PEmp and PEE) when compared with nonemphysematous lung (normals, PE1, PE2, and PE3) consistent with altered ventilation. Coarse texture in vascular lung demonstrated a descending trend in entropy (or ascending trend in uniformity) for normals, followed by embolism only (PE1, PE2, and PE3) and finally for emphysematous lung (PEmp and PEE) suggesting a correlation with degree of vascularity (or perfusion). 3D images of ventilated and vascular lung texture highlighted mismatched and matched defects in patients with pulmonary disorders. CONCLUSIONS: This feasibility study demonstrated that 3D filtered texture analysis can potentially provide correlates for ventilated and vascular lung, which may be useful in the diagnosis of PE in the presence of other causes of altered vascularity.

Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival.

RATIONALE AND OBJECTIVES: Perfusion imaging of the liver has attracted interest as a potential means for earlier detection of hepatic metastases, but the techniques are complex and do not form part of routine imaging protocols. This study assesses whether the hemodynamic status of the liver of patients with colorectal cancer but apparently normal hepatic morphology is reflected by texture features within a single portal-phase contrast enhanced computed tomography (CT) image and correlates texture with overall survival. MATERIALS AND METHODS: Portal-phase CT images from 27 patients with colorectal cancer but no apparent hepatic metastases were processed using a band-pass filter that highlighted image features at different spatial frequencies. A range of parameters reflecting liver texture on filtered images were correlated against CT hepatic perfusion index (HPI) and patient survival. RESULTS: After image filtration, entropy values from hepatic regions were inversely correlated with HPI (r=-0.503978, P=.007355), and directly correlated with survival (r=0.489642, P=.009533). An entropy value below 2.0 identified four patients who died within 36 months of their CT scan with sensitivity 100% and specificity 65% (P<.03). CONCLUSION: The hemodynamic status of the liver is reflected by subtle changes in coarse texture on portal phase images that can be revealed by texture analysis. Texture analysis has the potential to identify colorectal cancer patients with an apparently normal portal phase hepatic CT but reduced survival.

In search of biologic correlates for liver texture on portal-phase CT.

RATIONALE AND OBJECTIVES: The acceptance of computer-assisted diagnosis (CAD) in clinical practice has been constrained by the scarcity of identifiable biologic correlates for CAD-based image parameters. This study aims to identify biologic correlates for computed tomography (CT) liver texture in a series of patients with colorectal cancer. MATERIALS AND METHODS: In 28 patients with colorectal cancer, total hepatic perfusion (THP), hepatic arterial perfusion, and hepatic portal perfusion (HPP) were measured using perfusion CT. Hepatic glucose use was also determined from positron emission tomography (PET) and expressed as standardized uptake value (SUV). A hepatic phosphorylation fraction index (HPFI) was determined from both SUV and THP. These physiologic parameters were correlated with CAD parameters namely hepatic densitometry, selective-scale, and relative-scale texture features in apparently normal areas of portal-phase hepatic CT. RESULTS: For patients without liver metastases, a relative-scale texture parameter correlated inversely with SUV (r = -0.587, P = .007) and, positively with THP (r = 0.512, P = .021) and HPP (r = 0.451, P = .046). However, this relative texture parameter correlated most significantly with HPFI (r = -0.590, P = .006). For patients with liver metastases, although not significant an opposite trend was observed between these physiologic parameters and relative texture features (THP: r < -0.4, HPFI: r > 0.35). CONCLUSION: Total hepatic blood flow and glucose metabolism are two distinct but related biologic correlates for liver texture on portal phase CT, providing a rationale for the use of hepatic texture analysis as a indicator for patients with colorectal cancer.

Markovian and autoregressive clutter-noise models for a pattern-recognition Wiener filter.

Most modem pattern recognition filters used in target detection require a clutter-noise estimate to perform efficiently in realistic situations. Markovian and autoregressive models are proposed as an alternative to the white-noise model that has so far been the most widely used. Simulations by use of the Wiener filter and involving real clutter scenes show that both the Markovian and the autoregressive models perform considerably better than the white-noise model. The results also show that both models are general enough to yield similar results with different types of real scenes.