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The aim of this article is to study the texture features of corpus callosum in patients with Alzheimer's disease (AD) and normal controls based on magnetic resonance images. Then explore the texture differences of different gender between groups. Texture analysis was performed on 18 AD patients and 18 normal controls (NC). Texture features extracted from gray level co-occurrence matrix and run-length matrix were analyzed and the statistic significance was tested between groups. The results showed that texture features of corpus callosum yielded significant differences between AD and NC group. Male and female had different texture characters. The results indicated that texture analysis could be a useful tool for the detection of pathological changes in AD and the pathological changes may be different in the two genders.

Adaptation to exercise training can affect bone marrow adiposity; muscle-fat distribution; and muscle volume, strength and architecture. The objective of this study was to identify exercise-load-associated differences in magnetic resonance image textures of thigh soft tissues between various athlete groups and non-athletes. Ninety female athletes representing five differently loading sport types (high impact, odd impact, high magnitude, repetitive low impact and repetitive non-impact), and 20 non-athletic clinically healthy female controls underwent magnetic resonance imaging. Five thigh muscles, subcutaneous fat and femoral bone marrow were analysed with co-occurrence matrix-based quantitative texture analysis at two anatomical levels of the dominant leg. Compared with the controls thigh muscle textures differed especially in high-impact and odd-impact exercise-loading groups. However, all sports appeared to modulate muscle textures to some extent. Fat tissue was found different among the low-impact group, and bone marrow was different in the high-impact group when compared to the controls. Exercise loading was associated with textural variation in magnetic resonance images of thigh soft tissues. Texture analysis proved a potential method for detecting apparent structural differences in the muscle, fat and bone marrow.

Background and Objective Brainstem analysis in Magnetic Resonance Images is essential to detect Alzheimer's condition in the preclinical stages. In this work, an attempt has been made to segment the brainstem in sagittal (2D) and volumetric (3D) images and evaluate texture changes to differentiate Alzheimer's disease (AD) stages. Method The images obtained from a public access database are spatial normalized, skull stripped and contrast enhanced. Morphological Reconstruction based Fast and Robust Fuzzy ‘C’ Means technique is used to cluster the brain tissue in preprocessed images into three groups namely cerebrospinal fluid, grey matter and white matter. Brainstem is segmented from the white matter tissue using connected component labelling. Texture features from volumetric and sagittal brainstem slices are extracted and its statistical significance is evaluated. Results Results show that the proposed approach is able to segment the brainstem from all the considered images. Variation in texture is observed to be less than 2% among sagittal brainstem slices. Additionally, midsagittal and volumetric features are correlated, suggesting that midsagittal brainstem structure gives an estimate of brainstem volume. Texture features extracted from midsagittal slice shows significant variation (p Conclusion Midsagittal brainstem texture features are able to capture the changes occurring in the early stages of disease condition. As the distinction of AD in preclinical stage is complex and clinically significant, this approach could be useful for early diagnosis of the disease.

Nonlinear optical microscopy (NLOM) was used as a noninvasive and label-free tool to detect and quantify the extent of the cartilage recovery. Two cartilage injury models were established in the outer ears of rabbits that created a different extent of cartilage recovery based on the presence or absence of the perichondrium. High-resolution NLOM images were used to measure cartilage repair, specifically through spectral analysis and image texture. In contrast to a wound lacking a perichondrium, wounds with intact perichondria demonstrated significantly larger TPEF signals from cells and matrix, coarser texture indicating the more deposition of type I collagen. Spectral analysis of cells and matrix can reveal the matrix properties and cell growth. In addition, texture analysis of NLOM images showed significant differences in the distribution of cells and matrix of repaired tissues with or without perichondrium. Specifically, the decay length of autocorrelation coefficient based on TPEF images is 11.2 ± 1.1 in Wound 2 (with perichondrium) and 7.5 ± 2.0 in Wound 1 (without perichondrium), indicating coarser image texture and faster growth of cells in repaired tissues with perichondrium (p < 0.05). Moreover, the decay length of autocorrelation coefficient based on collagen SHG images also showed significant difference between Wound 2 and 1 (16.2 ± 1.2 vs. 12.2 ± 2.1, p < 0.05), indicating coarser image texture and faster deposition of collagen in repaired tissues with perichondrium (Wound 2). These findings suggest that NLOM is an ideal tool for studying cartilage repair, with potential applications in clinical medicine. NLOM can capture macromolecular details and distinguish between different extents of cartilage repair without the need for labelling agents.

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.

International audience; The potential of multispectral VIS-NIR imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred and forty muscles of three types (Longissimus thoracis, Biceps femoris and Semimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d'Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred and forty cube images were collected with nineteen emitting LEDs (405 to 1050 nm) using the Videometer Lab2 device. The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the PLSDA performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered. This study demonstrated the promising potential of the VIS-NIR multispectral imager to characterize beef meat muscles based on muscle type and its animal origin.

Magnetic resonance imaging (MRI) measurements are essential for the diagnosis of pelvic organ prolapse given the inaccuracy of clinical examination. However, MRI pelvic floor measurements are currently performed manually and can be inconsistent and time-consuming. In this paper, we present a scheme for semi-automatic measurement modeling on MRI based on image segmentation and intersecting point identification methods. The segmentation algorithm is a multi-stage mechanism based on block grouping, support vector machine classification, morphological operation and prior shape information. Block grouping is achieved by classifying blocks as bone or background based on image texture features. The classified blocks are then used to find the initial segmentation by the first phase morphological opening. Prior shape information is incorporated into the initial segmentation to obtain the final segmentation using registration with the similarity type transformation. After segmentation, points of reference that are used for pelvic floor measurements are identified using morphological skeleton operation. The experiments on the MRI images show that the presented scheme can detect the points of reference on the pelvic floor structure to determine the reference lines needed for the assessment of pelvic organ prolapse. This will lead towards more consistent and faster pelvic organ prolapse diagnosis on dynamic MRI studies, and possible screening procedures for predicting predisposition to pelvic organ prolapse by radiologic evaluation of pelvic floor measurements.

The human heart has a heterogeneous structure, which is characterized by different cell types and their spatial configurations. The physical structure, especially the fibre orientation and the interstitial fibrosis, determines the electrical excitation and in further consequence the contractility in macroscopic as well as in microscopic areas. Modern image processing methods and parameters could be used to describe the image content and image texture. In most cases the description of the texture is not satisfying because the fibre orientation, detected with common algorithms, is biased by elements such as fibrocytes or endothelial nuclei. The goal of this work is to figure out if cardiac tissue can be analysed and classified on a microscopic level by automated image processing methods with a focus on an accurate detection of the fibre orientation. Quantitative parameters for identification of textures of different complexity or pathological attributes inside the heart were determined. The focus was set on the detection of the fibre orientation, which was calculated on the basis of the cardiomyocytes' nuclei. It turned out that the orientation of these nuclei corresponded with a high precision to the fibre orientation in the image plane. Additionally, these nuclei also indicated very well the inclination of the fibre.

Summary Ultrasound (US) is an important tool for diagnosing of many musculoskeletal tissue conditions. Image texture analysis can be used to characterize this tissue. The complexity curve (CC) is a technique commonly used to characterize the number of grey-level transitions in an image. Variability and reliability of US texture measures in the muscle tissue are unavailable in the literature. The aim of this study was to determine the variability and reliability of five CC texture parameters from US images of healthy Biceps Brachialis and Gastrocnemius Lateralis (GL) muscles, with longitudinal and transversal orientations of the probe. Eight images per subject were obtained for 30 men in 2 days. Mean, standard deviation, coefficient of variation and intraclass correlation coefficient for the five parameters were calculated for regions of interest. Results showed that the variability was similar for both muscles and most of the parameters showed satisfactory reliability (r > 0·7) for the Biceps Brachialis with the transverse scan and for the GL with the longitudinal scan.

Many pathologies are age-related, e.g., cardiovascular disease generally occurs in midlife and cancer later in life. This suggests that aging predisposes the body to pathology. Plasma protein spatial distribution images of rat mesentery extracellular matrix (ECM) show texture due to the ECM structures, and there is an age-related decrease in tissue protein that may be related to matrix structure changes. The objective of this study was to compare changes in protein image texture under two conditions: superfusion with normal saline solution and aging. The decrease in soluble protein concentration during superfusion is a washout process, while the mechanism for the age-related decrease in tissue protein is unknown. Therefore, effects of aging and superfusion on tissue protein image texture were compared. Spatial co-occurrence matrix and Fourier analysis techniques have been used for texture evaluation. Superfused images showed a more uniform protein texture. There were gradual age-related changes in image texture parameters. Entropy increased with age from 140 to 630 days, indicating that protein distribution became more disorganized. The results suggest that changes in protein image texture are due to age-related alterations in matrix structure because removing only protein by superfusion had opposite effects on texture parameters.

Optical coherence tomography (OCT) has revolutionises the diagnosis of retinal disease based on the detection of microscopic rather than subcellular changes in retinal anatomy. However, currently the technique is limited to the detection of microscopic rather than subcellular changes in retinal anatomy. However, coherence based imaging is extremely sensitive to both changes in optical contrast and cellular events at the micrometer scale, and can generate subtle changes in the spectral content of the OCT image. Here we test the hypothesis that OCT image speckle (image texture) contains information regarding otherwise unresolvable features such as organelle changes arising in the early stages of neuronal degeneration. Using ultrahigh resolution (UHR) OCT imaging at 800 nm (spectral width 140 nm) we developed a robust method of OCT image analyses, based on spatial wavelet and texture-based parameterisation of the image speckle pattern. For the first time we show that this approach allows the non-invasive detection and quantification of early apoptotic changes in neurons within 30 min of neuronal trauma sufficient to result in apoptosis. We show a positive correlation between immunofluorescent labelling of mitochondria (a potential source of changes in cellular optical contrast) with changes in the texture of the OCT images of cultured neurons. Moreover, similar changes in optical contrast were also seen in the retinal ganglion cell- inner plexiform layer in retinal explants following optic nerve transection. The optical clarity of the explants was maintained throughout in the absence of histologically detectable change. Our data suggest that UHR OCT can be used for the non-invasive quantitative assessment of neuronal health, with a particular application to the assessment of early retinal disease.

Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion at a deteriorating joint. Detection and quantification of osteophytes from computed tomography (CT) images is helpful in assessing disease status as well as treatment and surgery planning. However, it is difficult to distinguish between osteophytes and healthy bones using simple thresholding or edge/texture features due to the similarity of their material composition. In this paper, we present a new method primarily based on the active shape model (ASM) to solve this problem and evaluate its application to the anterior cruciate ligament transaction (ACLT) rabbit femur model via micro-CT imaging. The common idea behind most ASM-based segmentation methods is to first build a parametric shape model from a training dataset and then apply the model to find a shape instance in a target image. A common challenge with such approaches is that a diseased bone shape is significantly altered at regions with osteophyte deposition misguiding an ASM method and eventually leading to suboptimum segmentations. This difficulty is overcome using a new partial-ASM method that uses bone shape over healthy regions and extrapolates it over the diseased region according to the underlying shape model. Finally, osteophytes are segmented by subtracting partial-ASM-derived shape from the overall diseased shape. Also, a new semiautomatic method is presented in this paper for efficiently building a 3-D shape model for an anatomic region using manual reference of a few anatomically defined fiducial landmarks that are highly reproducible on individuals. Accuracy of the method has been examined on simulated phantoms while reproducibility and sensitivity have been evaluated on micro-CT images of 2-, 4- and 8-week post-ACLT and sham-treated rabbit femurs. Experimental results have shown that the method is highly accurate (R\bm 2=0.99), reproducible (ICC = 0.97), and sensitive in detecting disease progression (p values: 0.065, 0.001, and

Purpose: Studies have shown that functional analysis of knee cartilage based on magnetic resonance (MR) relaxation times is a valuable tool in the understanding of osteoarthritis (OA). In this work, the regional spatial distribution of knee cartilage T1ρ and T2 relaxation times based on texture and laminar analyses was studied to investigate if they provide additional insight compared to global mean values in the study of OA. Methods: Knee cartilage of 36 subjects, 19 healthy controls and 17 with mild OA, was divided into 16 compartments. T1ρ and T2 relaxation times were studied with first order statistics, eight texture parameters with four different orientations using gray-level co-occurrence matrices and by subdividing each compartment into two different layers: Deep and superficial. Receiver operating characteristic curve analysis was performed to evaluate the potential of each technique to correctly classify the populations. Results: Although the deep and superficial cartilage layers had in general significantly different T1ρ and T2 relaxation times, they performed similarly in terms of subject discrimination. The subdivision of lateral and medial femoral compartments into weight-bearing and non-weight-bearing regions did not improve discrimination. Also it was found that the most sensitive region was the patella and that T1ρ discriminated better than T2. The most important finding was that with respect to global mean values, laminar and texture analyses improved subject discrimination. Conclusions: Results of this study suggest that spatially assessing MR images of the knee cartilage relaxation times using laminar and texture analyses could lead to better and probably earlier identification of cartilage matrix abnormalities in subjects with OA.

background: The precise assessment of embryo viability is an extremely important factor for the optimization of IVF treatments. In order to assess embryo viability, several embryo scoring systems have been developed. However, they rely mostly on a subjective visual analysis of embryo morphological features and thus are subject to inter- and intra-observer variation. In this paper, we propose a method for image segmentation (the dividing of an image into its meaningful constituent regions) and classification of human blastocyst images with the aim of automating embryo grading. methods: The delineation of the boundaries (segmentation) of the zona pellucida, trophectoderm (TE) and inner cell mass (ICM) were performed using advanced image analysis techniques (level set, phase congruency and fitting of ellipse methods). The fractal dimension and mean thickness of TE and ICM image texture descriptors (texture spectrum and grey-level run lengths) were calculated to characterize the main morphological features of the blastocyst with the aim of automatic grading using Support Vector Machine classifiers. results: The fractal dimension calculated from the delineated TE boundary provided a good indication of cell number (presented a 0.81 Pearson correlation coefficient with the number of cells), a feature closely associated with blastocyst quality. The classifiers showed different accuracy levels for each grade. They presented accuracy ranges from 0.67 to 0.92 for the embryo development classification, 0.67 –0.82 for the ICM classification and 0.53 –0.92 for the TE classification. The value 0.92 was the highest accuracy achieved in the tests with 73 blastocysts. conclusions: Semi-automatic grading of human blastocysts by a computer is feasible and may offer a more precise comparison of embryos, reducing subjectivity and allowing embryos with apparently identical morphological scores to be distinguished.

Background Disc degeneration quantification is important for monitoring the effects of new therapeutic methods, such as cell and growth factor therapy. Magnetic resonance (MR) image texture reflects biochemical and structural tissue properties and has been used for differentiating between normal and pathological status in a variety of medical applications. Purpose To investigate the suitability of textural descriptors for the quantification of intervertebral disc degeneration using conventional T2-weighted magnetic resonance images of the lumbar spine. Material and Methods A 3 Tesla scanner was used, and conventional T2- weighted MR images were obtained, and a total of 255 lumbar discs were analyzed. An atlas-based method was used for segmenting the disc regions from the images. A set of first and second order statistics describing texture of each region were calculated. The validity and reliability of these descriptors for disc degeneration severity quantification was tested through their correlation with patient age and qualitative clinical grading of degeneration severity. Texture quantification results were compared to a widely accepted method for disc degeneration quantification based on the measurement of disc's mean signal intensity. Results Out of the set of texture descriptors tested, two descriptors quantifying image intensity inhomogeneity, i.e. the grey level standard deviation and co-occurrence derived sum of squares displayed the strongest association to patient age and clinical grading of disc degeneration severity ( P < 0.001). This is attributed to these inhomogeneity descriptors' capability to capture the progressive loss of nucleus-annulus distinction in the degenerative progress. Statistical analysis indicates that these descriptors can effectively separate between early stages of degeneration. Quantitative measurements are highly repeatable (intraclass correlation >0.98). Conclusion Inhomogeneity descriptors could be a valuable tool for tracking the evolution of disc degeneration and monitoring the response to treatment in a simple, precise and repeatable manner.

A new method of detecting structured changes in trabecular bone, such as those associated with osteoporosis, was evaluated on magnetic resonance images of the wrist. The method was based on gray-scale morphological granulometries which classify image texture by iteratively filtering an image and measuring the rate of change of structural diminution in a filtered-image sequence. A classification scheme capable of distinguishing structural changes in trabecular bone starting from normal trabeculae through sclerotic, cystic, and grossly porotic bone is presented. Results of the application of this technique to the evaluation of high resolution magnetic resonance images of the wrist are presented.

Purpose: In multiphase contrast-enhanced magnetic resonance imaging (CE-MRI), liver segmentation is an important preprocessing step for the computer-aided evaluation of liver disease. However, because of the liver's irregular shape, proximity to surrounding organs, and large intensity variation, and peripheral contrast enhancement in the kidney, liver segmentation has been very challenging. This paper presents a novel hybrid active contour model and overall procedures that are specific to liver segmentation. Methods: The authors introduced an edge-function-scaled (weighted) region-based active contour model (ESRAC) and utilization of registered, multiphase sequences to address leakage-to-kidney and oversegmentation problems. The model incorporated weighted regional information with a compactly supported edge map, computed from a combination of images obtained during arterial and delayed phases, and it was coupled with a geodesic edge term. To cope with signal-inhomogeneity on MRI, all of the axial slices were partitioned into eight sectors with an angular span of 45°, centered on the inferior vena cava, each in the superior and inferior regions and the regional information regarding ESRAC was computed for each partition, henceforth the so-called partitioned ESRAC (p-ESRAC). Initialization of the active contour was performed by thresholding with a range of [200, +∞) and simple morphological operation during the delayed phase. At the end, to fill the holes in the segmented images caused by high gradients around the vasculature, noise, or outstanding texture features, iterative morphological operations were performed until convergence. The authors compared the segmentation accuracy of p-ESRAC to that with geodesic active contour, region-based active contour, geodesic active region, and localized region-based active contour using quantitative and visual assessments. Results: In three-dimensional experimental studies conducted on 30 subjects (14 normal or benign cases and 16 malignant cases), compared with other active contours, p-ESRAC achieved the highest dice similarity coefficients of 93.9 ± 1.6% (normal) and 91.6 ± 2.2% (malignant), respectively. In addition, p-ESRAC resulted in the lowest false positive rates of 4.5 ± 3.2% (normal) and 7.9 ± 3.0% (malignant), demonstrating it to be the most effective in reducing oversegmentation. The partition scheme improved segmentation accuracy by 5.4 ± 9.2% (normal) and 22.2 ± 27.6% (malignant) of the true segmentation that was missed by ESRAC. Visual assessment confirmed that p-ESRAC prevented leakage of the segmentation results of the liver into the kidney. Conclusions: A novel active-contour model was developed, allowing for accurate liver segmentation on multiphase CE-MRI, with conditions that include signal inhomogeneity and weak boundary conditions. Such a technique could be useful for applications that involve computer-aided diagnosis of liver disease.

Some basic quantitative analysis techniques of digitized high resolution atomic image are developed in this paper. We describe how to divide the atomic image into small independent areas with special structure information by the valley mesh segmentation method. The procedure is with regard to the bright contrast spot segmentation of atomic images. We suggest several ways for bright contrast spot localization in images. Calculation routines for peak detection and weighted local position average methods for this purpose are given. (C) 1996 Wiley-Liss, Inc.

A system is considered for segmenting noisy intensity images and consequent three-dimensional object reconstruction from a set of planar contours. A new semi-automatic method for the extraction of contours from a sequence of cross-sectional images based on an active contour model (ACM) is proposed. The dynamic ACM proceeds along the sequence of cross-sections following a non-rigid motion, in accordance with the organ boundary. Image texture information is also employed in the model. Problems associated with topological reconstruction from planar contours are addressed, and several criteria promoting semi-automatic topological reconstruction are introduced. The proposed system is successfully applied to the processing of real data related to animal embryonic organs, proving that the system allows detailed modelling of irregular objects. The reconstructed models can be observed in wire-frame, solid, transparent or stereoscopic semi-transparent format. The human-computer interaction implemented in the procedure assists with problems of feature identification and object manipulation about an arbitrary axis.

This article proposes a study on applied the texture analysis method to classify the different disease groups that are normal, tendon inflammation, calcific tendonitis and rotator cuff tear. The supraspinatus tendon is usually involved among above-mentioned diseases progression. Four texture analysis methods that texture feature coding method, gray-level cooccurrence matrix, fractal dimension and texture spectrum are used to extract features of tissue characteristic of supraspinatus tendon. The mutual information method is independently used to select powerful feature among four texture analysis method, further, the radial basis function network to classify the ones into the four disease group. Experimental results tested on 85 images reveal that the proposed system can achieves 84% accurate rate.

The purpose of this work was to study specific texture properties of the brain's white matter (WM) based on conventional high-resolution T1-weighted magnetic resonance imaging (MRI) datasets. Quantitative parameters anisotropy and laminarity were derived from 3-D texture analysis. Differences in WM texture associated with gender were evaluated on an age-matched sample of 210 young healthy subjects (mean age 24.8, SD 3.97 years, 103 males and 107 females). Changes of WM texture with age were studied using 112 MRI-T1 datasets of healthy subjects aged 16 to 70 years (57 males and 55 females). Both texture measures indicated a "more regular" WM structure in females (p10(-6)). An age-related deterioration of WM structure manifests itself as a remarkable decline of both parameters (p10(-6)) that is more prominent in females (p10(-6)) than in males (p = 0.02). Texture analysis of anatomical MRI-T1 brain datasets provides quantitative information about macroscopic WM characteristics and helps discriminating between normal and pathological aging.

It has been previously shown that intensity and volume features of the hippocampus from MR images of the brain are useful in detecting the abnormality and consequently candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. In this paper, we have studied and compared volume and texture features of hippocampus for TLE lateralization. Volume and some features describing the intensity non-uniformities are used. We use manual segmentation of the hippocampi from T1 weighted images and map them to the FLAIR images. Wavelet features as well as mean and variance of the hippocampus intensities of the FLAIR slices are used as texture features. A dataset of 55 patients with 28 right and 27 left abnormal sides was used in the experiments. Experimental results show that the volume and intensity features are useful in determining the abnormal side.

This paper describes the application of an amplitude modulation-frequency modulation (AM-FM) image representation in segmenting electron micrographs of skeletal muscle for the recognition of: 1) normal sarcomere ultrastructural pattern and 2) abnormal regions that occur in sarcomeres in various myopathies. A total of 26 electron micrographs from different myopathies were used for this study. It is shown that the AM-FM image representation can identify normal repetitive structures and sarcomeres, with a good degree of accuracy. This system can also detect abnormalities in sarcomeres which alter the normal regular pattern, as seen in muscle pathology, with a recognition accuracy of 75%-84% as compared to a human expert. © 2000 IEEE. 19 12 1253 1258 Cited By :24

We postulated that the analysis of regional image texture in two-dimensional (2D) echocardiograms would be an accurate method to differentiate normal from abnormal myocardial structure. We tested this hypothesis with quantitative texture measures to study the regional, spatial distribution of echo amplitudes in 2D echocardiograms performed before and immediately after blunt left chest trauma was induced in six anesthetized dogs. After trauma the contused region of myocardium appeared brighter and exhibited an altered myocardial texture. By use of a set quantitative texture measures, we found no significant differences in pretrauma images when normal regions were compared with regions to be contused. Also, we found no difference when we compared the normal regions in each animal in pretrauma vs posttrauma images. Twelve measures, however, differentiated normal from contused regions within the posttrauma images (p values ranged from .0057 to .0001 by multivariate analysis of variance). These texture measures were capable of differentiating normal from abnormal tissue only when texture along the azimuthal (lateral) direction was calculated. We conclude that regions of myocardial contusion exhibit visibly altered local echo-amplitude patterns (altered image texture) and that these image texture alterations may be quantified with digital image analysis techniques. These findings suggest that quantitative texture calculations may be a useful approach to ultrasound tissue characterization.

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.