A segmentation algorithm for a robotic micro

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Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Segmentation algorithms for detecting microcalcifications in mammograms Abstract: The presence of microcalcification clusters in mammograms contributes evidence for the diagnosis of early stages of breast cancer. In many cases, microcalcifications are subtle and their detection can benefit from an automated system serving as a diagnostic aid.

The potential contribution of such a system may become more significant as the number of mammograms screened increases to levels that challenge the capacity of radiology clinics. Many techniques for detecting microcalcifications start with a segmentation algorithm that indicates all candidate structures for the subsequent phases. Most algorithms used to segment microcalcifications have aspects that might raise operational difficulties, such as thresholds or windows that must be selected, or parametric models of the data.

We present a new segmentation algorithm and compare it to two other algorithms: the multi-tolerance region-growing algorithm, which operates without the aspects mentioned above, and the active contour model, which has not been applied previously to segment microcalcifications.

The new algorithm operates without threshold or window selection or parametric data models, and it is more than an order of magnitude faster than the other two.

Article :. Date of Publication: June DOI: Need Help?Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

AI Finally Makes Micro Segmentation a Reality for Financial Marketers

Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials Abstract: Most policy search PS algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot.

This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials a dozen and a few minutes? A second strategy is to create data-driven surrogate models of the expected reward e. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots, designing generic priors, and optimizing the computing time.

Article :. Date of Publication: 27 December DOI: Need Help?This paper presents a geometrical feature detection framework for use with conventional 2D laser rangefinders. This framework is composed of three main procedures: data pre-processing, breakpoint detection and line extraction. In data pre-processing, low-level data organization and processing are discussed, with emphasis to sensor bias compensation. Breakpoint detection allows to determine sequences of measurements which are not interrupted by scanning surface changing.

Two breakpoint detectors are investigated, one based on adaptive thresholding, and the other on Kalman filtering. Implementation and tuning of both detectors are also investigated. Line extraction is performed to each continuous scan sequence in a range image by applying line kernels. We have investigated two classic kernels, commonly used in mobile robots, and our Split-and-Merge Fuzzy SMF line extractor. SMF employs fuzzy clustering in a split-and-merge framework without the need to guess the number of clusters.

Qualitative and quantitative comparisons using simulated and real images illustrate the main characteristics of the framework when using different methods for breakpoint and line detection.

These comparisons illustrate the characteristics of each estimator, which can be exploited according to the platform computing power and the application accuracy requirements. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Arras, K.

Barni, M. Bar-Shalom, Y. Google Scholar. Bezdek, J. Borenstein, J. Peters, Wellesley, MA. Borges, G. Castellanos, J. Jamshidi, F. Pin and P. Dauchez eds. Duda, R. Einsele, T. Forsberg, J. Frigui, H. Haralick, R.

Line Extraction in 2D Range Images for Mobile Robotics

Jazwinski, A. Jensfelt, P.Smart use of data moves the needle in financial marketing. And in a highly competitive market with few net new prospects, financial institutions need to push the value of customer data to the max.

One of the traditional mainstays of financial marketing, segmentation, may soon be eclipsed by a new approach — artificial intelligence-driven micro-segmentation. Bank and credit union marketers target consumers based on family composition, life stage and zip code to find high-probability groups that are more likely to be in the market for mortgage, personal loan or investment products. This is a great first step towards targeted marketing.

Traditional marketing segmentation is the art of thinking in terms of large groups. These groups receive more targeted marketing campaigns, where the campaign profiles and customer groups match up. In micro-segmentation we are taking the traditional segmentation approach a step further. Micro-segmentation is a more advanced type of segmentation which is able to create groups containing small numbers of customers to form extremely precise and targetable segments.

The credit union used the machine learning software from Amplero to automatically create and understand micro-segments and determine the best messages — subject line, image, content and offers — to drive increased applications, which was the goal of the campaign. Traditional segmentations lean heavier on a limited number of large groups using a combination of relatively static profile data relating to geography and demographics.

For current clients this is sometimes extended with an RFM analysis. RFM — Recency, Frequency and Monetary value — is a marketing technique that analyses customer and sales data to find your most valuable customers, based on their past purchasing habits.

It is used to establish groups based on customer value. The question is: How homogeneous are these segments? It seems like there is an inherent bias into what would make a good segmentation model — until proven otherwise.

Using data to calculate the mean discount rates per customer and analyzing customer value and profitability is hugely impactful. You can segment customers by mean discount rate and use this for targeting during a current sale. For instance, a challenge banks and credit unions face is to effectively educate older customers on the benefits of mobile banking, while at the same time capturing tech-savvy clients with their mobile banking capabilities as a salespoint and offering competitive products.

You want to pinpoint the appropriate proposition for a segment and target them — and the look-a-likes — using the right message. With customer profiles, the more data you can work with, the better. The diagram shows the basic flow of micro-segmentation. The system proceeds through a number of automated steps: ingesting the data from different sources, analyzing it, generating the segments and the offer variants and then running the campaigns. A feedback loop with campaign outcomes is essential so the system can learn what works and optimize the segments and campaigns accordingly.

You can imagine that the more refined the messaging and therefore the smaller the segments the more effective micro-segmentation is.

How to Get Started with Micro-Segmentation

Think about it — your database is made up of individuals, each with their own motivations, interests, profile, habits, etc. In micro-segmentation we are taking the traditional segmentation approach up a notch by layering-in more data into the model and assembling new segments out of fresh combinations of data.

The smaller size of the segments and subsegments allow marketers to be even more focused about their messaging.Accurate segmentation of blood vessels plays an important role in the computer-aided diagnosis and interventional treatment of vascular diseases.

The statistical method is an important component of effective vessel segmentation; however, several limitations discourage the segmentation effect, i. In addition, the mixture models of the statistical methods are constructed relaying on the characteristics of the image histograms.

Thus, it is a challenging issue for the traditional methods to be available in vessel segmentation from multi-modality angiographic images. To overcome these limitations, a flexible segmentation method with a fixed mixture model has been proposed for various angiography modalities.

Our method mainly consists of three parts. Firstly, multi-scale filtering algorithm was used on the original images to enhance vessels and suppress noises. As a result, the filtered data achieved a new statistical characteristic. Secondly, a mixture model formed by three probabilistic distributions two Exponential distributions and one Gaussian distribution was built to fit the histogram curve of the filtered data, where the expectation maximization EM algorithm was used for parameters estimation.

Finally, three-dimensional 3D Markov random field MRF were employed to improve the accuracy of pixel-wise classification and posterior probability estimation.

a segmentation algorithm for a robotic micro

To quantitatively evaluate the performance of the proposed method, two phantoms simulating blood vessels with different tubular structures and noises have been devised. Meanwhile, four clinical angiographic data sets from different human organs have been used to qualitatively validate the method.

To further test the performance, comparison tests between the proposed method and the traditional ones have been conducted on two different brain magnetic resonance angiography MRA data sets.

The results of the phantoms were satisfying, e. According to the opinions of clinical vascular specialists, the vessels in various data sets were extracted with high accuracy since complete vessel trees were extracted while lesser non-vessels and background were falsely classified as vessel. In the comparison experiments, the proposed method showed its superiority in accuracy and robustness for extracting vascular structures from multi-modality angiographic images with complicated background noises.

The experimental results demonstrated that our proposed method was available for various angiographic data. The main reason was that the constructed mixture probability model could unitarily classify vessel object from the multi-scale filtered data of various angiography images.

The advantages of the proposed method lie in the following aspects: firstly, it can extract the vessels with poor angiography quality, since the multi-scale filtering algorithm can improve the vessel intensity in the circumstance such as uneven contrast media and bias field; secondly, it performed well for extracting the vessels in multi-modality angiographic images despite various signal-noises; and thirdly, it was implemented with better accuracy, and robustness than the traditional methods.

Generally, these traits declare that the proposed method would have significant clinical application. Nowadays, cardio- and cerebro-vascular diseases have greatly threatened human health. Since the use of imaging techniques such as computed tomography angiography CTA and magnetic resonance angiography MRA in minimally invasive surgery, high quality image segmentation has become an important area of interest.

A detailed review has been made on threshold based, pattern recognition based, and deformable models based segmentation algorithms which were used for medical images [ 12 ]. The main tendency of these algorithms with their principle ideas, application field, advantages and disadvantages were discussed.

Conclusion has been drawn that each segmentation method with improvement, or in combination with other technique, could provide better performance [ 34 ]. Clustering algorithms and supervised classification used for the segmentation of atherosclerotic plaques were analysed in [ 5 ]. For the diagnosis and treatment of vascular diseases, it is critical to accurately extract and quantify the blood vessels from the angiographic image.

Aimed at the extraction of blood vessels, different kinds of segmentation methods have been proposed, e. Multi-scale filtering can enhance the intensity of the vessels while suppresses that of the background [ 16 ] in various angiographic data, however, further processing should be conducted in order to label the vessel class out.

Deformable models well integrate bottom—up information and top—down priori knowledge, but the segmentation quality mainly depends on the model parameters [ 1718 ]. For statistical models, the vessels in the angiographic data with a given modality are segmented according to the intensity distributions of anatomical structures, by which voxels with overlapped intensities will inevitably be misclassified [ 19 ]. In order to reduce the probability of error classification, hybrid methods have been proposed to take the spatial contextual information into account [ 20 ], but limitations still exist for multi-modality angiographic images.

So far, statistical models have drawn a lot of attention, and model selection is an important issue in this kind of vessel segmentation techniques.Understanding the complete structure of acinus is necessary to measure the pathway of gas exchange and to simulate various mechanical phenomena in the lungs.

The usual manual segmentation of a complete acinus structure from their experimentally obtained images is difficult and extremely time-consuming, which hampers the statistical analysis. In this study, we develop a semiautomatic segmentation algorithm for extracting the complete structure of acinus from synchrotron micro-CT images of the closed chest of mouse lungs.

The algorithm uses a combination of conventional binary image processing techniques based on the multiscale and hierarchical nature of lung structures. Specifically, larger structures are removed, while smaller structures are isolated from the image by repeatedly applying erosion and dilation operators in order, adjusting the parameter referencing to previously obtained morphometric data. A cluster of isolated acini belonging to the same terminal bronchiole is obtained without floating voxels.

The run time is drastically shortened compared with manual methods.

a segmentation algorithm for a robotic micro

These findings suggest that our method may be useful for taking samples used in the statistical analysis of acinus. The mammalian respiratory system can be separated into two functional zones: conducting and respiratory.

The conducting zone, as an airway tree, comprises abundant branching tubes originating from the trachea, dividing dichotomously into the bronchi and bronchioles, and ending in the terminal bronchioles. Between the conducting zone and the respiratory zone, there is an intermediary region called the transitional bronchiole.

The most precise definition is that the pulmonary acinus comprises the branched complex of alveolated airways that are connected to the same first order respiratory or transitional bronchiole [ 1 ]. Exploring the depth of the lung, for example, microstructure of the acinus, is significant for the characterization of the respiratory system at both the structural and functional level, in particular, from the viewpoint of biomechanics.

The microstructure of the lung is harder to reconstruct and visualize relative to that of the conducting airways. Common X-ray CT images lack resolution for imaging microscale subjects, so the technique is unavailable to visualize fine structures of the lung [ 2 ].

Previously, silicone rubber cast models 3D [ 3 ] and serial histological section reconstruction 2D-3D [ 45 ] have been used to visualize the structure of the lung parenchyma.

a segmentation algorithm for a robotic micro

These approaches can provide the morphological information of pulmonary acinus. However, both approaches have limitations when they are used to reconstruct the 3D structure of fine lung parenchyma for biomechanical simulation. Recently, advances in micro-CT [ 67 ] and synchrotron micro-CT imaging [ 8 — 11 ] have made it possible to visualize the in situ lung anatomical structure in 3D with micrometer resolution. Because synchrotron radiation gives a much higher flux with a collimated X-ray beam compared with laboratory microfocus X-ray sources, the contrast of a synchrotron image is higher than that of a conventional X-ray source.

Even though the required image resolution was available, the identification segmentation of the acinus structure in synchrotron micro-CT images was still hard to achieve because of the complexity of the porous structure and the razor-thin membrane wall. A reconstruction method of a certain region of the lung parenchyma has been published [ 10 ], but it is not an entire functional structure. A complete structure of the acinus is necessary to measure the pathway of gas exchange and to simulate gas diffusion, tissue deformation, and air particle deposition [ 12 — 14 ].

Commonly, segmentation of the acinus structure is performed manually by an expert but is very tedious and time-consuming. In such situations, it is impractical to carry out the statistical analysis for investigating pulmonary morphology and function. Therefore, a segmentation technique for rapidly extracting the entire acinus structure is desired.

PoseMap for Localization in Urban Environments - IROS18

Here, we propose a semi-automatic segmentation algorithm for extracting pulmonary microstructures from three-dimensional synchrotron micro-CT images. Improvements of basic dilation, region growing, and erosion techniques are used to achieve extraction of various scales of airway structures, such as terminal bronchioles and acini.Micro-segmentation is a marketing strategy that uses data to identify the interests of specific individuals and influence their thoughts or actions.

However, the ideal is not always our reality, so we need to determine new strategies for addressing client needs. However, you can also establish base audiences on the following attributes:. Identify highly indexing variables within each base audience: Next, you should obtain in-depth highly indexing attributes for each base audience. Highly indexing variables can be identified through audience analytics within your preferred DMP or another data platform.

These highly indexing variables allow for a more holistic understanding of each base audience and their potential subgroups, so that you can identify the distinguishing characteristics and translate these subgroups into micro-segments. After examining highly indexing attributes, group them together primarily psychographic and behavioral to identify micro-segments. To ensure good coverage, you should create at least three to four micro-segments for each base audience.

For instance, Adults without Children may index highly for attributes associated with upscale travel and shopping, healthy living, national pride, and cultural arts and entertainment — leading to the development of the following micro-segments:. In some cases, you may have several base audiences that share the same highly indexing variables and, thus, the same micro-segments.

At this point, you should be able to identify your most relevant segments, including: lifestyle, interests, attitudes, purchase behavior, buyer stage, and other attributes. While the families in both micro-segments have Pre-K children, you could tailor messaging for these two Pre-K micro-segments that is significantly different from one other.

A micro-segmentation marketing strategy can allow for the layering of numerous data points — identifying a robust mosaic of hundreds or even thousands of micro-segments for more focused, relevant targeting.

This kind of relevancy is key to people-based marketing, as it is centered on targeting your customers with more focused messaging. Micro-segmentation is one of the important ways that you can bring that relevancy to the forefront of your marketing strategy and, as shown, you can develop your micro-segmentation strategy regardless of client and data limitations. We use cookies. You have options.

a segmentation algorithm for a robotic micro

Cookies help us keep the site running smoothly and inform some of our advertising, but how we use them is entirely up to you. Accept our recommended settings or customise them to your wishes. How to Get Started with Micro-Segmentation.

However, you can also establish base audiences on the following attributes: Geography: Country, region, population growth, density Demographics: Age, gender, education, income, presence of children, ethnicity, marital status Psychographic: Lifestyle, values, social class, personality Behavioral: Usage, loyalties, awareness, liking, purchase patterns, price sensitivity Identify highly indexing variables within each base audience: Next, you should obtain in-depth highly indexing attributes for each base audience.

Create micro-segments : After examining highly indexing attributes, group them together primarily psychographic and behavioral to identify micro-segments. Join the Discussion. View the discussion thread. Authored by: Kaitlin Woskoff Show bio. Published on: July 31, Get instant blog updates by email.


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