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Contrast-enhanced X-ray imaging provides a non-destructive and flexible approach to optimizing contrast in soft tissues, especially when incorporated with Lugol’s solution (aqueous I2KI), a technique currently referred to as diffusible iodine-based contrast-enhanced computed tomography (diceCT). This stain exhibits high rates of penetration and results in excellent contrast between and within soft tissues, including the central nervous system. Here, we present a staining method for optimizing contrast in the brain of a cartilaginous fish, the brownbanded bamboo shark, Chiloscyllium punctatum, and a bony fish, the common goldfish, Carassius auratus, usingdiceCT.Theaim of this optimization procedure is to provide suitable contrast between neural tissue and background tissue(s) of the head, thereby facilitating digital segmentation and volumetric analysis of the central nervous system. Both species were scanned before staining and were rescanned at time (T) intervals, either every 48 h (C. punctatum) or every 24 h (C. auratus), to assess stain penetration and contrast enhancement. To compare stain intensities, raw X-ray CT data were reconstructed using air and water calibration phantoms that were scanned under identical conditions to the samples. Optimal contrast across the brain was achieved at T = 240 h for C. punctatum and T=96 h for C. auratus. Higher resolution scans of the whole brain were obtained at the two optimized staining times for all the corresponding specimens. The use of diceCT provides a new and valuable tool for visualizing differences in the anatomic organization of both the central and peripheral nervous systems of fish.

We present a method for simulating lower quality images starting from higher quality ones, based on acquired image pairs from different optical setups. The method does not require estimates of point (or line) spread functions of the system, but utilizes the relative transfer function derived from images of real specimen of interest in the observed application. Thanks to the use of a larger number of real specimen, excellent stability and robustness of the method is achieved. The intended use is exploring the influence of image quality on features and classification accuracy in pattern recognition based screening tasks. Visual evaluation of the obtained images strongly confirms usefulness of the method. The approach is quantitatively evaluated by observing stability of feature values, proven useful for PAP-smear classification, between synthetic and real images from seven different microscope setups. The evaluation shows that features from the synthetically generated lower resolution images are as similar to features from real images at that resolution, as features from two different images of the same specimen, taken at the same low resolution, are to each other.

This paper presents a novel structural approach to quantitatively characterising nuclear chromatin texture in light microscope images of Pap smears. The approach is based on segmenting the chromatin into blob-like primitives and characterising their properties and arrangement. The segmentation approach makes use of multiple focal planes. It comprises two basic steps: (i) mean-shift filtering in the feature space formed by concatenating pixel spatial coordinates and intensity values centred around the best all-in-focus plane; and (ii) hierarchical marker-based watershed segmentation. The paper also presents an empirical evaluation of the approach based on the classification of 43 routine clinical Pap smears. Two variants of the approach were compared to a reference approach (employing extended depth-of-field rather than mean-shift) in a feature selection/classification experiment, involving 138 segmentation-based features, for discriminating normal and abnormal slides. The results demonstrate improved performance over the reference approach. The results of a second feature selection/classification experiment, including additional classes of features from the literature, show that a combination of the proposed structural and conventional features yields a classification performance of 0.919 +/- 0.015 (AUC +/- Std.Dev.). Overall the results demonstrate the efficacy of the proposed structural approach and confirm that it is indeed possible to detect malignancy associated changes (MACs) in conventional Papanicolaou stain.

Objectives: The objective of this study was to measure the efficacy of 7 new spatiotemporal features for discriminating between benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging (MRI) of the breast. Methods: A total of 48 breast lesions from 39 patients were used: 25 malignant and 23 benign. Lesions were acquired using 1.5-T MRI machines in 3 different protocols. Two experiments were performed: (i) selection of the most discriminatory subset of features drawn from the new features and features from the literature and (ii) validation of classification performance of the selected subset of features. Results: Results of the feature selection experiment show that the subset comprising 2 of the new features is the most useful for automatic classification of suspicious lesions in the breast: (i) gradient correlation of maximum intensity and (ii) mean wash-in rate. Results of the validation experiment show that using these 2 features, unseen data can be classified with an area under the receiver operating characteristic curve of 0.91 ± 0.06. Conclusions: Results of the experiments suggest that suspicious lesions in dynamic contrast-enhanced-MRI of the breast can be classified, with high accuracy, using only 2 of the proposed spatiotemporal features. The selected features indicate heterogeneity of enhancement and speed of enhancement in a tissue. High values of these indicators are likely to be correlated with malignancy. Copyright © 2011 by Lippincott Williams & Wilkins.

The image Foresting (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned correct segmentation labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. Here, we propose the relaxed IFT (RIFT). This modified version of the IFT features an additional parameter to control the smoothness of the segmentation boundary. The RIFT yields more intuitive segmentation results in the presence of noise and weak edges, while maintaining a low computational complexity. We show an application of the method to the refinement of manual segmentations of a thoracolumbar muscle in magnetic resonance images. The performed study shows that the refined segmentations are qualitatively similar to the manual segmentations, while intra-user variations are reduced by more than 50%.

This paper compares several camera calibration methods on the estimation of specific extrinsic and intrinsic parameters. Good estimates of the chosen parameters, rotation and radial lens distortion are essential to increase the accuracy of quantitative measurements and to accurately stitch single field of view-based images together. The parameters are obtained using two selected methods on different objective magnifications on a microscope system using a fixed grid calibration pattern. We evaluate two methods and show that the rotation angles from one of the methods is consistent with a simple homography while the other estimates a consistently smaller angle. The radial distortion estimates are both very small and relate to a distortion of less than one pixel. © 2010 IEEE.

The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).