Fast computation of morphological operations with arbitrary structuring elements

Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM), k-means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

Validation of JCountPro software for efficient assessment of ionizing radiation-induced foci in human lymphocytes

PURPOSE

Ionizing radiation-induced foci (IRIF) known also as DNA repair foci represent the most sensitive and specific assay for assessing DNA double-strand break (DSB). IRIF are usually visualized and enumerated with the aid of fluorescence microscopy using antibodies to phosphorylated γH2AX and 53BP1. Although several approaches and software packages were developed for quantification of IRIF, not one of them was commonly accepted and inter-laboratory variability in the outputs was reported. In this study, JCountPro software was validated for IRIF enumeration in two independent laboratories.

MATERIALS AND METHODS

Human lymphocytes were γ-irradiated at doses of 0, 2, 5, 10 and 50 cGy. The cells were fixed, permeabilized and IRIF were immunostained using appropriate antibodies. Cell images were acquired with automatic Metafer system. Endogenous and radiation-induced γH2AX and 53BP1 foci were enumerated using JCountPro. This analysis was performed from the same cell galleries by the researchers from two laboratories. Yield of foci was analyzed by either arithmetic mean (AM) value (foci/cell) or principal average (PA) derived from the approximation of foci distribution with Poisson statistics. Statistical analysis was performed using factorial ANOVA.

RESULTS

Enumeration of 53BP1, γH2AX and co-localized 53BP1/γH2AX foci by JCountPro was essentially the same between laboratories. IRIF were detected at all doses and linear dose response was obtained in the studied dose range. PA values from Poisson distribution fitted the data better as compared to AM values and were more powerful and sensitive for IRIF analysis than the AM values. All JCountPro data were confirmed by visual focus enumeration.

CONCLUSIONS

We concluded that the JCountPro software was efficient in objectively enumerating IRIF regardless of an individual researcher's bias and has a potential for usage in clinics and molecular epidemiology.

Multiple linear feature detection based on multiple-structuring-element center-surround top-hat transform

Linear feature detection is an important technique in different applications of image processing. To detect linear features in different types of images, a simple but effective algorithm based on a multiple-structuring-element center-surround top-hat transform is proposed. The center-surround top-hat transform is discussed and analyzed. Based on the properties of this transform for image feature detection, multiple structuring elements are constructed corresponding to the possible linear features at different directions. The whole algorithm is divided into four parts. First, the algorithm uses the center-surround top-hat transform to detect all the possible linear features at different directions through constructing multiple structuring elements. Second, the detected linear feature regions at each direction are processed by a closing operation to remove the possible holes or unconnected regions. Third, the processed results of the detected linear feature regions at all directions are combined to form all the possible detected linear feature regions. Fourth, the combined result is refined by using some simple operations to form the final result. Experimental results on different types of images from different applications verified the effective performance of the proposed algorithm. Moreover, the experimental results indicate that the proposed algorithm could be used in different applications.

Mineral image enhancement based on sequential combination of toggle and top-hat based contrast operator

Enhancing mineral image especially making mineral image details clear is very useful for mineral analysis. To effectively enhance mineral image, an algorithm based on the toggle contrast operator and top-hat based contrast operator is proposed in this paper. Sequentially combining the toggle contrast operator and top-hat based contrast operator could be used to identify image features especially the image details. So, appropriately exacting the identified image features by the sequentially combined toggle and top-hat based contrast operator is important for mineral image enhancement, which is analyzed firstly in this paper. After that, the multi-scale extension of feature extraction is given and used to construct the final features for mineral image enhancement. By importing the final extracted image features into the original mineral image through contrast enlargement, the original mineral image is well enhanced and the mineral image details are very clear. Experimental results on different types of mineral images verified the effective performance of the proposed algorithm.

A new design tool for feature extraction in noisy images based on grayscale hit-or-miss transforms

The hit-or-miss transform (HMT) is a well-known morphological transform capable of identifying features in digital images. When image features contain noise, texture, or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank-order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT.

γH2AX foci as a measure of DNA damage: a computational approach to automatic analysis

The γH2AX focus assay represents a fast and sensitive approach for the detection of one of the critical types of DNA damage - double-strand breaks (DSB) induced by various cytotoxic agents including ionising radiation. Apart from research applications, the assay has a potential in clinical medicine/pathology, such as assessment of individual radiosensitivity, response to cancer therapies, as well as in biodosimetry. Given that generally there is a direct relationship between numbers of microscopically visualised γH2AX foci and DNA DSB in a cell, the number of foci per nucleus represents the most efficient and informative parameter of the assay. Although computational approaches have been developed for automatic focus counting, the tedious and time consuming manual focus counting still remains the most reliable way due to limitations of computational approaches. We suggest a computational approach and associated software for automatic focus counting that minimises these limitations. Our approach, while using standard image processing algorithms, maximises the automation of identification of nuclei/cells in complex images, offers an efficient way to optimise parameters used in the image analysis and counting procedures, optionally invokes additional procedures to deal with variations in intensity of the signal and background in individual images, and provides automatic batch processing of a series of images. We report results of validation studies that demonstrated correlation of manual focus counting with results obtained using our computational algorithm for mouse jejunum touch prints, mouse tongue sections and human blood lymphocytes as well as radiation dose response of γH2AX focus induction for these biological specimens.

Binary morphology with spatially variant structuring elements: algorithm and architecture

Mathematical morphology with spatially variant structuring elements outperforms translation-invariant structuring elements in various applications and has been studied in the literature over the years. However, supporting a variable structuring element shape imposes an overwhelming computational complexity, dramatically increasing with the size of the structuring element. Limiting the supported class of structuring elements to rectangles has allowed for a fast algorithm to be developed, which is efficient in terms of number of operations per pixel, has a low memory requirement, and a low latency. These properties make this algorithm useful in both software and hardware implementations, not only for spatially variant, but also translation-invariant morphology. This paper also presents a dedicated hardware architecture intended to be used as an accelerator in embedded system applications, with corresponding implementation results when targeted for both field programmable gate arrays and application specific integrated circuits.

Robust semi-automated path extraction for visualising stenosis of the coronary arteries

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets.

Adaptive border marching algorithm: automatic lung segmentation on chest CT images

Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

Efficient 2-D grayscale morphological transformations with arbitrary flat structuring elements

An efficient algorithm is presented for the computation of grayscale morphological operations with arbitrary 2-D flat structuring elements (S.E.). The required computing time is independent of the image content and of the number of gray levels used. It always outperforms the only existing comparable method, which was proposed in the work by Van Droogenbroeck and Talbot, by a factor between 3.5 and 35.1, depending on the image type and shape of S.E. So far, filtering using multiple S.E.s is always done by performing the operator for each size and shape of the S.E. separately. With our method, filtering with multiple S.E.s can be performed by a single operator for a slightly reduced computational cost per size or shape, which makes this method more suitable for use in granulometries, dilation-erosion scale spaces, and template matching using the hit-or-miss transform. The discussion focuses on erosions and dilations, from which other transformations can be derived.

Semi-Automatic Integrated Segmentation Approaches and Contour Extraction Applied to Computed Tomography Scan Images

We propose to segment two-dimensional CT scans traumatic brain injuries with various methods. These methods are hybrid, feature extraction, level sets, region growing, and watershed which are analysed based upon their parametric and nonparametric arguments. The pixel intensities, gradient magnitude, affinity map, and catchment basins of these methods are validated based upon various constraints evaluations. In this article, we also develop a new methodology for a computational pipeline that uses bilateral filtering, diffusion properties, watershed, and filtering with mathematical morphology operators for the contour extraction of the lesion in the feature available based mainly on the gradient function. The evaluations of the classification of these lesions are very briefly outlined in this context and are being undertaken by pattern recognition in another paper work.

Segmentation of abdominal ultrasound images of the prostate using a priori information and an adapted noise filter

This article discusses a method for the automatic segmentation of trans-abdominal ultrasound images of the prostate. Segmentation begins with the application of a filter to enhance the contours without modifying the image information. It combines adaptive morphological filtering and median filtering to detect the noise-containing regions and smooth them. A heuristic optimization algorithm searches for the contour initialized from a prostate model. The performance of the algorithm was tested by comparing the resulting contours with those obtained by manual segmentation. The average distance between the contours was 2.5 mm and the average coverage index was 93%.