Graphical Model-Based Vertebra Identification from X-Ray Image(s)

Automated identification of vertebrae from X-ray image(s) is an important step for various medical image computing tasks such as 2D/3D rigid and non-rigid registration. In this chapter we present a graphical model-based solution for automated vertebra identification from X-ray image(s). Our solution does not ask for a training process using training data and has the capability to automatically determine the number of vertebrae visible in the image(s). This is achieved by combining a graphical model-based maximum a posterior probability (MAP) estimate with a mean-shift based clustering. Experiments conducted on simulated X-ray images as well as on a low-dose low quality X-ray spinal image of a scoliotic patient verified its performance.

Static analysis of WCET in a satellite software subsystem

This paper describes the authors? experience with static analysis of both WCET and stack usage of a satellite on-board software subsystem. The work is a continuation of a previous case study that used a dynamic WCET analysis tool on an earlier version of the same software system. In particular, the AbsInt aiT tool has been evaluated by analysing both C and Ada code generated by Simulink within the UPMSat-2 project. Some aspects of the aiT tool, specifically those dealing with SPARC register windows, are compared to another static analysis tool, Bound-T. The results of the analysis are discussed, and some conclusions on the use of static WCET analysis tools on the SPARC architecture are commented in the paper.

Automated characterisation of intergrowth textures in mineral particles. A case study

The characterisation of mineral texture has been a major concern for process mineralogists, as liberation characteristics of the ores are intimately related to the mineralogical texture. While a great effort has been done to automatically characterise texture in unbroken ores, the characterisation of textural attributes in mineral particles is usually descriptive. However, the quantitative characterisation of texture in mineral particles is essential to improve and predict the performance of minerallurgical processes (i.e. all the processes involved in the liberation and separation of the mineral of interest) and to achieve a more accurate geometallurgical model. Driven by this necessity of achieving a more complete characterisation of textural attributes in mineral particles, a methodology has been recently developed to automatically characterise the type of intergrowth between mineral phases within particles by means of digital image analysis. In this methodology, a set ofminerallurgical indices has been developed to quantify different mineralogical features and to identify the intergrowth pattern by discriminant analysis. The paper shows the application of the methodology to the textural characterisation of chalcopyrite in the rougher concentrate of the Kansanshi copper mine (Zambia). In this sample, the variety of intergrowth patterns of chalcopyrite with the other minerals has been used to illustrate the methodology. The results obtained show that the method identifies the intergrowth type and provides quantitative information to achieve a complete and detailed mineralogical characterisation. Therefore, the use of this methodology as a routinely tool in automated mineralogy would contribute to a better understanding of the ore behaviour during liberation and separation processes.

New perspectives on quantitative textural analysis

Process mineralogy provides the mineralogical information required by geometallurgists to address the inherent variation of geological data. The successful benefitiation of ores mostly depends on the ability of mineral processing to be efficiently adapted to the ore characteristics, being liberation one of the most relevant mineralogical parameters. The liberation characteristics of ores are intimately related to mineral texture. Therefore, the characterization of liberation necessarily requieres the identification and quantification of those textural features with a major bearing on mineral liberation. From this point of view grain size, bonding between mineral grains and intergrowth types are considered as the most influential textural attributes. While the quantification of grain size is a usual output of automated current technologies, information about grain boundaries and intergrowth types is usually descriptive and difficult to quantify to be included in the geometallurgical model. Aiming at the systematic and quantitative analysis of the intergrowth type within mineral particles, a new methodology based on digital image analysis has been developed. In this work, the ability of this methodology to achieve a more complete characterization of liberation is explored by the analysis of chalcopyrite in the rougher concentrate of the Kansanshi copper-gold mine (Zambia). Results obtained show that the method provides valuable textural information to achieve a better understanding of mineral behaviour during concentration processes. The potential of this method is enhanced by the fact that it provides data unavailable by current technologies. This opens up new perspectives on the quantitative analysis of mineral processing performance based on textural attributes.

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

Automated morphometry provides accurate and reproducible virtual staging of liver fibrosis in chronic hepatitis C

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Morphological analysis of pitting corrosion in AISI 310S austenitic stainless steel after salt spray exposition

This paper aims to study evolution of increase, distribution and classification of pits in 310S austenitic stainless steels obtained in the state as-received and heat-treated under different exposure times in saline. This work applicability has been based on a technique development for morphologic characterization of localized corrosion associated with description aspects of shapes, size and population-specific parameters. Methodology has been consisted in the following steps: specimens preparation, corrosion tests via salt spray in different conditions, microstructural analysis, pits profiles analysis and images analysis, digital processing and image analysis in order to characterize the pits distribution, morphology and size. Results obtained in digital processing and profiles image analysis have been subjected to statistical analysis using median as parameter in the alloy as received and treated. The alloy as received displays the following morphology: hemispheric pits> transition region A> transition region B> irregular> conic. The pits amount in the treated alloy at each exposure time is: transition region B> hemispherical> transition region A> conic> irregular.

Proteomic analysis of normal and malignant prostate tissue to identify novel proteins lost in cancer

BACKGROUND. Alterations of important protein pathways, including loss of prostate secretory granules, and disruption of the prostatic secretory pathway have been identified as early events in malignancy. In this study, proteomics was used to map the differences in protein expression between normal and malignant prostate tissues and to identify and analyze differentially expressed proteins in human prostate tissue with particular regard to the proteins lost in malignancy. METHODS. Small quantities of normal and malignant prostate tissue were taken fresh from 34 radical prostatectomy cases. After histological examination, proteins were solubilized from selected tissues and separated using two-dimensional electrophoresis. Using image analysis, the proteome of normal and malignant tissues were mapped and differentially expressed proteins (present in normal and absent in malignant tissue) were identified and subsequently analyzed using peptide mass finger printing and N-terminal sequencing. Western blotting and immunohistochemistry were performed to examine expression profiles and tissue localization of candidate proteins. RESULTS. Comparison of protein maps of normal and malignant prostate were used to identify 20 proteins which were lost in malignant transformation, including prostate specific antigen (PSA), alpha-l antichymotrypsin (ACT), haptoglobin, and lactoylglutathione lyase. Three of the 20 had not previously been reported in human prostate tissue (Ubiquitin-like NEDD8, calponin, and a follistatin-related protein). Western blotting confirmed differences in the expression profiles of NEDD8 and calponin, and immunohistochemistry demonstrated differences in the cellular localization of these two proteins in normal and malignant prostate glands. CONCLUSIONS. The expression of NEDD8, calponin, and the follistatin-related protein in normal prostate tissues is a novel finding and the role of these important functional proteins in normal prostate and their loss or reduced expression in prostate malignancy warrants further investigations. (C) 2002 Wiley-Liss, Inc.

Cortical Thickness Reduction of Normal Appearing Cortex in Patients with Polymicrogyria

OBJECTIVE To examine cortical thickness and volumetric changes in the cortex of patients with polymicrogyria, using an automated image analysis algorithm. METHODS Cortical thickness of patients with polymicrogyria was measured using magnetic resonance imaging (MRI) cortical surface-based analysis and compared with age-and sex-matched healthy subjects. We studied 3 patients with disorder of cortical development (DCD), classified as polymicrogyria, and 15 controls. Two experienced neuroradiologists performed a conventional visual assessment of the MRIs. The same data were analyzed using an automated algorithm for tissue segmentation and classification. Group and individual average maps of cortical thickness differences were produced by cortical surface-based statistical analysis. RESULTS Patients with polymicrogyria showed increased thickness of the cortex in the same areas identified as abnormal by radiologists. We also identified a reduction in the volume and thickness of cortex within additional areas of apparently normal cortex relative to controls. CONCLUSIONS Our findings indicate that there may be regions of reduced cortical thickness, which appear normal from radiological analysis, in the cortex of patients with polymicrogyria. This finding suggests that alterations in neuronal migration may have an impact in the cortical formation of the cortical areas that are visually normal. These areas are associated or occur concurrently with polymicrogyria.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

Microvascular Lymphatic Density Analysis in Cutaneous Regressive and Nonregressive Superficial Spreading Melanomas Using the Lymphatic Marker D2-40

Background: The prognostic significance of spontaneous regression in melanoma, especially thin lesions, has been a controversial issue for the past 20 years, although recent studies suggest that extensive and late regression may be related to worse prognosis. Many data suggest that lymphangiogenesis predicts metastatic spread in melanoma. Methods: We have quantified lymphatic microvascular density (LMVD) in thin (<= 1.0 mm) superficial spreading melanomas comparing regressive and nonregressive melanomas, regressive and nonregressive areas from the same tumor, and early and late histological stages of regression in the same tumor. In addition, we tried to correlate lymphangiogenesis and tumor growth phase. We conducted histological examinations and immunohistochemical analyses using monoclonal antibody D2-40 with subsequent quantification by image analysis of 37 melanomas, 16 regressive and 21 nonregressive (controls). Results: We found higher LMVD in the late stage of regression compared with nonregressive area (internal control) of regressive melanomas. Conclusions: Our study suggest that the late stage of spontaneous regression in thin melanomas may be related to worse prognosis as it showed higher LMVD, and evidence shows that this is related with increased risk of metastatic spread. But this supposition must be confirmed by a longer follow-up for detection of lymph node metastases.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.