Bridging the segmentation theory/practice divide

More than thirty years ago, Wind's seminal review of research in market segmentation culminated with a research agenda for the subject area. In the intervening period, research has focused on the development of segmentation bases and models, segmentation research techniques and the identification of statistically sound solutions. Practical questions about implementation and the integration of segmentation into marketing strategy have received less attention, even though practitioners are known to struggle with the actual practice of segmentation. This special issue is motivated by this tension between theory and practice, which has shaped and continues to influence the research priorities for the field. Although many years may have elapsed since Wind's original research agenda, pressing questions about effectiveness and productivity apparently remain; namely: (i) concerns about the link between segmentation and performance, and its measurement; and (ii) the notion that productivity improvements arising from segmentation are only achievable if the segmentation process is effectively implemented. There were central themes to the call for papers for this special issue, which aims to develop our understanding of segmentation value, productivity and strategies, and managerial issues and implementation.

Export promotion policies in CARICOM: main issues, effects and implications

Includes bibliography

The Latin American experience in pension system reform: coverage, fiscal issues and possible implications for China

Includes bibliography

Pension legislation and gender equity in Latin America

Includes bibliography

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

A new method for the estimation of the reverberation time from measured room impulse responses with application to Italian opera houses

The Schroeder's backward integration method is the most used method to extract the decay curve of an acoustic impulse response and to calculate the reverberation time from this curve. In the literature the limits and the possible improvements of this method are widely discussed. In this work a new method is proposed for the evaluation of the energy decay curve. The new method has been implemented in a Matlab toolbox. Its performance has been tested versus the most accredited literature method. The values of EDT and reverberation time extracted from the energy decay curves calculated with both methods have been compared in terms of the values themselves and in terms of their statistical representativeness. The main case study consists of nine Italian historical theatres in which acoustical measurements were performed. The comparison of the two extraction methods has also been applied to a critical case, i.e. the structural impulse responses of some building elements. The comparison underlines that both methods return a comparable value of the T30. Decreasing the range of evaluation, they reveal increasing differences; in particular, the main differences are in the first part of the decay, where the EDT is evaluated. This is a consequence of the fact that the new method returns a “locally" defined energy decay curve, whereas the Schroeder's method accumulates energy from the tail to the beginning of the impulse response. Another characteristic of the new method for the energy decay extraction curve is its independence on the background noise estimation. Finally, a statistical analysis is performed on the T30 and EDT values calculated from the impulse responses measurements in the Italian historical theatres. The aim of this evaluation is to know whether a subset of measurements could be considered representative for a complete characterization of these opera houses.

Automated cement segmentation in vertebroplasty

Vertebroplasty is a minimally invasive procedure with many benefits; however, the procedure is not without risks and potential complications, of which leakage of the cement out of the vertebral body and into the surrounding tissues is one of the most serious. Cement can leak into the spinal canal, venous system, soft tissues, lungs and intradiscal space, causing serious neurological complications, tissue necrosis or pulmonary embolism. We present a method for automatic segmentation and tracking of bone cement during vertebroplasty procedures, as a first step towards developing a warning system to avoid cement leakage outside the vertebral body. We show that by using active contours based on level sets the shape of the injected cement can be accurately detected. The model has been improved for segmentation as proposed in our previous work by including a term that restricts the level set function to the vertebral body. The method has been applied to a set of real intra-operative X-ray images and the results show that the algorithm can successfully detect different shapes with blurred and not well-defined boundaries, where the classical active contours segmentation is not applicable. The method has been positively evaluated by physicians.

Seeing Spaces: An Eye-Tracking Study Of Speech Segmentation

Speech is often a multimodal process, presented audiovisually through a talking face. One area of speech perception influenced by visual speech is speech segmentation, or the process of breaking a stream of speech into individual words. Mitchel and Weiss (2013) demonstrated that a talking face contains specific cues to word boundaries and that subjects can correctly segment a speech stream when given a silent video of a speaker. The current study expanded upon these results, using an eye tracker to identify highly attended facial features of the audiovisual display used in Mitchel and Weiss (2013). In Experiment 1, subjects were found to spend the most time watching the eyes and mouth, with a trend suggesting that the mouth was viewed more than the eyes. Although subjects displayed significant learning of word boundaries, performance was not correlated with gaze duration on any individual feature, nor was performance correlated with a behavioral measure of autistic-like traits. However, trends suggested that as autistic-like traits increased, gaze duration of the mouth increased and gaze duration of the eyes decreased, similar to significant trends seen in autistic populations (Boratston & Blakemore, 2007). In Experiment 2, the same video was modified so that a black bar covered the eyes or mouth. Both videos elicited learning of word boundaries that was equivalent to that seen in the first experiment. Again, no correlations were found between segmentation performance and SRS scores in either condition. These results, taken with those in Experiment, suggest that neither the eyes nor mouth are critical to speech segmentation and that perhaps more global head movements indicate word boundaries (see Graf, Cosatto, Strom, & Huang, 2002). Future work will elucidate the contribution of individual features relative to global head movements, as well as extend these results to additional types of speech tasks.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Clinical validation of an automated vessel-segmentation software of the extracranial-carotid arteries based on 3D-MRA: A prospective study

OBJECTIVES: To determine the accuracy of automated vessel-segmentation software for vessel-diameter measurements based on three-dimensional contrast-enhanced magnetic resonance angiography (3D-MRA). METHOD: In 10 patients with high-grade carotid stenosis, automated measurements of both carotid arteries were obtained with 3D-MRA by two independent investigators and compared with manual measurements obtained by digital subtraction angiography (DSA) and 2D maximum-intensity projection (2D-MIP) based on MRA and duplex ultrasonography (US). In 42 patients undergoing carotid endarterectomy (CEA), intraoperative measurements (IOP) were compared with postoperative 3D-MRA and US. RESULTS: Mean interoperator variability was 8% for measurements by DSA and 11% by 2D-MIP, but there was no interoperator variability with the automated 3D-MRA analysis. Good correlations were found between DSA (standard of reference), manual 2D-MIP (rP=0.6) and automated 3D-MRA (rP=0.8). Excellent correlations were found between IOP, 3D-MRA (rP=0.93) and US (rP=0.83). CONCLUSION: Automated 3D-MRA-based vessel segmentation and quantification result in accurate measurements of extracerebral-vessel dimensions.

Semi-automatic segmentation of fetal cardiac cavities: progress towards an automated fetal echocardiogram

OBJECTIVE: To develop a novel application of a tool for semi-automatic volume segmentation and adapt it for analysis of fetal cardiac cavities and vessels from heart volume datasets. METHODS: We studied retrospectively virtual cardiac volume cycles obtained with spatiotemporal image correlation (STIC) from six fetuses with postnatally confirmed diagnoses: four with normal hearts between 19 and 29 completed gestational weeks, one with d-transposition of the great arteries and one with hypoplastic left heart syndrome. The volumes were analyzed offline using a commercially available segmentation algorithm designed for ovarian folliculometry. Using this software, individual 'cavities' in a static volume are selected and assigned individual colors in cross-sections and in 3D-rendered views, and their dimensions (diameters and volumes) can be calculated. RESULTS: Individual segments of fetal cardiac cavities could be separated, adjacent segments merged and the resulting electronic casts studied in their spatial context. Volume measurements could also be performed. Exemplary images and interactive videoclips showing the segmented digital casts were generated. CONCLUSION: The approach presented here is an important step towards an automated fetal volume echocardiogram. It has the potential both to help in obtaining a correct structural diagnosis, and to generate exemplary visual displays of cardiac anatomy in normal and structurally abnormal cases for consultation and teaching.

BEYOND ROOTS ALONE: NOVEL METHODOLOGIES FOR ANALYZING COMPLEX SOIL AND MINIRHIZOTRON IMAGERY USING IMAGE PROCESSING AND GIS TOOLS

Quantifying belowground dynamics is critical to our understanding of plant and ecosystem function and belowground carbon cycling, yet currently available tools for complex belowground image analyses are insufficient. We introduce novel techniques combining digital image processing tools and geographic information systems (GIS) analysis to permit semi-automated analysis of complex root and soil dynamics. We illustrate methodologies with imagery from microcosms, minirhizotrons, and a rhizotron, in upland and peatland soils. We provide guidelines for correct image capture, a method that automatically stitches together numerous minirhizotron images into one seamless image, and image analysis using image segmentation and classification in SPRING or change analysis in ArcMap. These methods facilitate spatial and temporal root and soil interaction studies, providing a framework to expand a more comprehensive understanding of belowground dynamics.

Finite element analysis in asymptomatic, symptomatic, and ruptured abdominal aortic aneurysms: in search of new rupture risk predictors.

OBJECTIVES To compare biomechanical rupture risk parameters of asymptomatic, symptomatic and ruptured abdominal aortic aneurysms (AAA) using finite element analysis (FEA). STUDY DESIGN Retrospective biomechanical single center analysis of asymptomatic, symptomatic, and ruptured AAAs. Comparison of biomechanical parameters from FEA. MATERIALS AND METHODS From 2011 to 2013 computed tomography angiography (CTA) data from 30 asymptomatic, 15 symptomatic, and 15 ruptured AAAs were collected consecutively. FEA was performed according to the successive steps of AAA vessel reconstruction, segmentation and finite element computation. Biomechanical parameters Peak Wall Rupture Risk Index (PWRI), Peak Wall Stress (PWS), and Rupture Risk Equivalent Diameter (RRED) were compared among the three subgroups. RESULTS PWRI differentiated between asymptomatic and symptomatic AAAs (p < .0004) better than PWS (p < .1453). PWRI-dependent RRED was higher in the symptomatic subgroup compared with the asymptomatic subgroup (p < .0004). Maximum AAA external diameters were comparable between the two groups (p < .1355). Ruptured AAAs showed the highest values for external diameter, total intraluminal thrombus volume, PWS, RRED, and PWRI compared with asymptomatic and symptomatic AAAs. In contrast with symptomatic and ruptured AAAs, none of the asymptomatic patients had a PWRI value >1.0. This threshold value might identify patients at imminent risk of rupture. CONCLUSIONS From different FEA derived parameters, PWRI distinguishes most precisely between asymptomatic and symptomatic AAAs. If elevated, this value may represent a negative prognostic factor for asymptomatic AAAs.

Fully Automatic CT Segmentation for Computer-Assisted Pre-operative Planning of Hip Arthroscopy

Extraction of both pelvic and femoral surface models of a hip joint from CT data for computer-assisted pre-operative planning of hip arthroscopy is addressed. We present a method for a fully automatic image segmentation of a hip joint. Our method works by combining fast random forest (RF) regression based landmark detection, atlas-based segmentation, with articulated statistical shape model (aSSM) based hip joint reconstruction. The two fundamental contributions of our method are: (1) An improved fast Gaussian transform (IFGT) is used within the RF regression framework for a fast and accurate landmark detection, which then allows for a fully automatic initialization of the atlas-based segmentation; and (2) aSSM based fitting is used to preserve hip joint structure and to avoid penetration between the pelvic and femoral models. Validation on 30 hip CT images show that our method achieves high performance in segmenting pelvis, left proximal femur, and right proximal femur surfaces with an average accuracy of 0.59 mm, 0.62 mm, and 0.58 mm, respectively.