Abnormal Microvascular Architecture, Fibrosis, and Pericyte Characteristics in the Calf Muscle of Peripheral Artery Disease Patients with Claudication and Critical Limb Ischemia.

Work from our laboratory documents pathological events, including myofiber oxidative damage and degeneration, myofibrosis, micro-vessel (diameter = 50-150 µm) remodeling, and collagenous investment of terminal micro-vessels (diameter ≤ 15 µm) in the calf muscle of patients with Peripheral Artery Disease (PAD). In this study, we evaluate the hypothesis that the vascular pathology associated with the legs of PAD patients encompasses pathologic changes to the smallest micro-vessels in calf muscle. Biopsies were collected from the calf muscle of control subjects and patients with Fontaine Stage II and Stage IV PAD. Slide specimens were evaluated by Quantitative Multi-Spectral and Fluorescence Microscopy. Inter-myofiber collagen, stained with Masson Trichrome (MT), was increased in Stage II patients, and more substantially in Stage IV patients in association with collagenous thickening of terminal micro-vessel walls. Evaluation of the Basement Membrane (BM) of these vessels reveals increased thickness in Stage II patients, and increased thickness, diameter, and Collagen I deposition in Stage IV patients. Coverage of these micro-vessels with pericytes, key contributors to fibrosis and BM remodeling, was increased in Stage II patients, and was greatest in Stage IV patients. Vascular pathology of the legs of PAD patients extends beyond atherosclerotic main inflow arteries and affects the entire vascular tree-including the smallest micro-vessels.

Fully unsupervised M-FISH chromosome image characterization.

Chromosome analysis is an important and difficult task for clinical diagnosis and biological research. A color imaging technique, multiplex fluorescent in situ hybridization (M-FISH), has been developed to ease the analysis of the process. Using an M-FISH technique each chromosome class (1,2, …,22,X,Y) is stained with a unique color. However, significant variations between images are observed due to a number of factors such as uneven hybridization and spectral overlap among channels. These types of variations influence the pixel classification accuracy of image classification methods which are supervised and require a set of annotated images for training. In this paper, we present a fully unsupervised M-FISH chromosome image classification methodology. Our main contributions are 1) the assumption that the intensity of a chromosome pixel is sampled from multiple Gaussian components [Gaussian mixture model (GMM)] such that each component corresponds to one chromosome class, and 2) the initialization of the GMM model using the emission information of each chromosome class. This is feasible since prior to the M-FISH image acquirement, we already know which chromosome class is emitting to each of the five M-FISH image channels. The method has been tested on a large number of M-FISH images and an overall accuracy of 89.85% is reported. Our method is unsupervised and presents higher classification accuracy even when it is compared with common supervised based methods. Since the developed classification method does not require training data, it is highly convenient when ground truth does not exist.

A hybrid plaque characterization method using intravascular ultrasound images.

BACKGROUND: Intravascular ultrasound (IVUS) is an invasive imaging modality that provides high resolution cross-sectional images permitting detailed evaluation of the lumen, outer vessel wall and plaque morphology and evaluation of its composition. Over the last years several methodologies have been proposed which allow automated processing of the IVUS data and reliable segmentation of the regions of interest or characterization of the type of the plaque. OBJECTIVE: In this paper we present a novel methodology for the automated identification of different plaque components in grayscale IVUS images. METHODS: The proposed method is based on a hybrid approach that incorporates both image processing techniques and classification algorithms and allows classification of the plaque into three different categories: Hard Calcified, Hard-Non Calcified and Soft plaque. Annotations by two experts on 8 IVUS examinations were used to train and test our method. RESULTS: The combination of an automatic thresholding technique and active contours coupled with a Random Forest classifier provided reliable results with an overall classification accuracy of 86.14%. CONCLUSIONS: The proposed method can accurately detect the plaque using grayscale IVUS images and can be used to assess plaque composition for both clinical and research purposes.

Segmentation of microarray images using pixel classification-comparison with clustering-based methods.

OBJECTIVE: DNA microarray technology yields expression profiles for thousands of genes, in a single hybridization experiment. The quantification of the expression level is performed using image analysis. In this paper we introduce a supervised method for the segmentation of microarray images using classification techniques. The method is able to characterize the pixels of the image as signal, background and artefact. METHODS AND MATERIAL: The proposed method includes five steps: (a) an automated gridding method which provides a cell of the image for each spot. (b) Three multichannel vector filters are employed to preprocess the raw image. (c) Features are extracted from each pixel of the image. (d) The dimension of the feature set is reduced. (e) Support vector machines are used for the classification of pixels as signal, background, artefacts. The proposed method is evaluated using both real images from the Stanford microarray database and simulated images generated by a microarray data simulator. The signal and the background pixels, which are responsible for the quantification of the expression levels, are efficiently detected. RESULTS: A quality measure (qindex) and the pixel-by-pixel accuracy are used for the evaluation of the proposed method. The obtained qindex varies from 0.742 to 0.836. The obtained accuracy for the real images is about 98%, while the accuracies for the good, normal and bad quality simulated images are 96, 93 and 71%, respectively. The proposed classification method is compared to clustering-based techniques, which have been proposed for microarray image segmentation. This comparison shows that the classification-based method reports better results, improving the performance by up to 20%. CONCLUSIONS: The proposed method can be used for segmentation of microarray images with high accuracy, indicating that segmentation can be improved using classification instead of clustering. The proposed method is supervised and it can only be used when training data are available.

A novel semiautomated atherosclerotic plaque characterization method using grayscale intravascular ultrasound images: comparison with virtual histology.

Intravascular ultrasound (IVUS) virtual histology (VH-IVUS) is a new technique, which provides automated plaque characterization in IVUS frames, using the ultrasound backscattered RF-signals. However, its computation can only be performed once per cardiac cycle (ECG-gated technique), which significantly decreases the number of characterized IVUS frames. Also atherosclerotic plaques in images that have been acquired by machines, which are not equipped with the VH software, cannot be characterized. To address these limitations, we have developed a plaque characterization technique that can be applied in grayscale IVUS images. Our semiautomated method is based on a three-step approach. In the first step, the plaque area [region of interest (ROI)] is detected semiautomatically. In the second step, a set of features is extracted for each pixel of the ROI and in the third step, a random forest classifier is used to classify these pixels into four classes: dense calcium, necrotic core, fibrotic tissue, and fibro-fatty tissue. In order to train and validate our method, we used 300 IVUS frames acquired from virtual histology examinations from ten patients. The overall accuracy of the proposed method was 85.65% suggesting that our approach is reliable and may be further investigated in the clinical and research arena.

Enhancement of multichannel chromosome classification using a region-based classifier and vector median filtering.

Multichannel chromosome image acquisition is used for cancer diagnosis and research on genetic disorders. This type of imaging, apart from aiding the cytogeneticist in several ways, facilitates the visual detection of chromosome abnormalities. However, chromosome misclassification errors result from different factors, such as uneven hybridization, spectral overlap among fluors, and biochemical noise. In this paper, we enhance the chromosome classification accuracy by making use of a region Bayes classifier that increases the classification accuracy when compared to the already developed pixel-by-pixel classifier and by incorporating the vector median filtering approach for filtering of the image. The method is evaluated using a publicly available database that contains 183 six-channel chromosome sets of images. The overall improvement on the chromosome classification accuracy is 9.99%, compared to the pixel-by-pixel classifier without filtering. This improvement in the chromosome classification accuracy would allow subtle deoxyribonucleic acid abnormalities to be identified easily. The efficiency of the method might further improve by using features extracted from each region and a more sophisticated classifier.

Comparison of quantitative coronary angiography with intracoronary ultrasound. Can quantitative coronary angiography accurately estimate the severity of a luminal stenosis?

In this study we investigated the accuracy of monoplane and biplane quantitative coronary angiography in estimating the luminal dimensions, using intracoronary ultrasound as gold standard. Biplane angiography and intracoronary ultrasound were performed in 24 arterial segments. The end-diastolic intracoronary ultrasound frames were manually selected and segmented. In 2 end-diastolic X ray projections, quantitative coronary angiography was performed and a novel methodology was applied to register the segmented frames onto the processed angiographic images. The luminal areas determined by quantitative coronary angiography in 1 (monoplane) and 2 projections (mean) were compared with those determined by intracoronary ultrasound. The obtained correlation coefficients for the monoplane and mean estimations were 0.69 +/- 0.12 and 0.77 +/- 0.08, respectively. It would appear that by increasing the angle between the biplane projections, the correlation between intracoronary ultrasound and mean estimations improves. Our results provide evidence that orthogonal biplane angiography is more reliable and should be preferred to assess luminal dimensions.

A classification-based segmentation of cDNA microarray images using Support Vector Machines.

Microarray technology provides a tool for the simultaneous analysis of the expression level for an amount of genes. Microarray studies have been shown that image processing techniques can significantly influence microarray data precision. In this paper we propose a supervised method for the segmentation of microarray images based on classification techniques. Support Vector machine is employed to classify each pixel of the image into signal, background or artefacts. In addition, a preprocessing step is applied in order to filter the initial image. The proposed method is applied both to real and simulated images. The pixels of the image are classified in two classes for the real images and three classes for the simulated one. For this task, an informative set of features is used from both green and red channels. The results obtained indicate high accuracy (approximately 99%).

A watershed based segmentation method for multispectral chromosome images classification.

M-FISH (multicolor fluorescence in situ hybridization) is a recently developed cytogenetic technique for cancer diagnosis and research on genetic disorders which uses 5 fluors to label uniquely each chromosome and a fluorescent DNA stain. In this paper, an automated method for chromosome classification in M-FISH images is presented. The chromosome image is initially decomposed into a set of primitive homogeneous regions through the morphological watershed transform applied to the image intensity gradient magnitude. Each segmented area is then classified using a Bayes classifier. We have evaluated our methodology on a commercial available M-FISH database. The classifier was trained and tested on non-overlapping chromosome images and an overall accuracy of 89% is achieved. By introducing feature averaging on watershed basins, the proposed technique achieves substantially better results than previous methods at a lower computational cost.