Mammogram Segmentation using Multi-atlas Deformable Registration

Accurate breast region segmentation is an important step in various automated algorithms involving detection of lesions like masses and microcalcifications, and efficient telemammography. Existing segmentation algorithms underperform due to variations in image quality and shape of the breast region. In this paper, we propose to segment breast region by combining data-driven clustering with deformable image registration. In the first phase of the approach, we identify atlas images from a dataset of mammograms using data-driven clustering. Then, we segment these atlas images and use in the next phase of the algorithm. The second phase is atlas-based registration. For a candidate image, we find the most similar atlas image from the set of atlases identified in phase one. We deform the selected atlas image to match the given test image using the Demon's registration algorithm. Then, the segmentation mask of the deformed atlas is transferred to the mammogram in consideration. Finally, we refine the segmentation mask with some morphological operations in order to obtain accurate breast region boundary. We evaluated the performance of our method using ground-truth segmentation masks verified by an expert radiologist. We compared the proposed method with three existing state-of-the-art algorithms for breast region segmentation and the proposed approach outperformed all three in most of the cases.

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COMBINING DATA-DRIVEN AND NUMERICAL MODELLING APPROACHES TO STORM EROSION PREDICTION

Coastal Engineering Proceedings ◽

10.9753/icce.v36v.sediment.38 ◽

2020 ◽

pp. 38

Author(s):

Joshua Simmons ◽

Kristen Splinter

Keyword(s):

Numerical Models ◽

Individual Performance ◽

Study Data ◽

Data Driven ◽

Erosion Prediction ◽

Trade Offs ◽

Combining Data ◽

Erosion Models ◽

Using Data ◽

The Individual

Physics-based numerical models play an important role in the estimation of storm erosion, particularly at beaches for which there is little historical data. However, the increasing availability of pre-and post-storm data for multiple events and at a number of beaches around the world has opened the possibility of using data-driven approaches for erosion prediction. Both physics-based and purely data-driven approaches have inherent strengths and weaknesses in their ability to predict storm-induced erosion. It is vital that coastal managers and modelers are aware of these trade-offs as well as methods to maximise the value from each modelling approach in an increasingly data-rich environment. In this study, data from approximately 40 years of coastal monitoring at Narrabeen-Collaroy Beach (SE Australia)has been used to evaluate the individual performance of the numerical erosion models SBEACH and XBeach, and a data-driven modelling technique. The models are then combined using a simple weighting technique to provide a hybrid estimate of erosion.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/v53dZiO8Y60

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Remote Cloud Ceiling Assessment Using Data-Mining Methods

Journal of Applied Meteorology ◽

10.1175/jam2177.1 ◽

2004 ◽

Vol 43 (12) ◽

pp. 1929-1946 ◽

Cited By ~ 5

Author(s):

Richard L. Bankert ◽

Michael Hadjimichael ◽

Arunas P. Kuciauskas ◽

William T. Thompson ◽

Kim Richardson

Keyword(s):

Data Mining ◽

Satellite Data ◽

Weather Prediction ◽

Ground Truth ◽

Physical Parameters ◽

Low Cloud ◽

Ceiling Height ◽

Mining Methods ◽

Using Data ◽

Automated Algorithms

Abstract Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.

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Using data-driven algorithms for semi-automated geomorphological mapping

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-021-02062-5 ◽

2021 ◽

Author(s):

Elisa Giaccone ◽

Fabio Oriani ◽

Marj Tonini ◽

Christophe Lambiel ◽

Grégoire Mariéthoz

Keyword(s):

Regional Scale ◽

Ground Truth ◽

Classification Problem ◽

Data Driven ◽

Training Dataset ◽

Geomorphological Mapping ◽

Direct Sampling ◽

Geomorphological Map ◽

Using Data ◽

Alpine Environments

AbstractIn this paper, we compare the performance of two data-driven algorithms to deal with an automatic classification problem in geomorphology: Direct Sampling (DS) and Random Forest (RF). The main goal is to provide a semi-automated procedure for the geomorphological mapping of alpine environments, using a manually mapped zone as training dataset and predictor variables to infer the classification of a target zone. The applicability of DS to geomorphological classification was never investigated before. Instead, RF based classification has already been applied in few studies, but only with a limited number of geomorphological classes. The outcomes of both approaches are validated by comparing the eight detected classes with a geomorphological map elaborated on the field and considered as ground truth. Both DS and RF give satisfactory results and provide similar performances in term of accuracy and Cohen’s Kappa values. The map obtained with RF presents a noisier spatial distribution of classes than when using DS, because DS takes into account the spatial dependence of the different classes. Results suggest that DS and RF are both suitable techniques for the semi-automated geomorphological mapping in alpine environments at regional scale, opening the way for further improvements.

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Solving inverse problems using data-driven models

Acta Numerica ◽

10.1017/s0962492919000059 ◽

2019 ◽

Vol 28 ◽

pp. 1-174 ◽

Cited By ~ 25

Author(s):

Simon Arridge ◽

Peter Maass ◽

Ozan Öktem ◽

Carola-Bibiane Schönlieb

Keyword(s):

Inverse Problems ◽

Industrial Applications ◽

Data Driven ◽

Analytical Models ◽

Survey Paper ◽

Domain Specific ◽

Combining Data ◽

Domain Specific Knowledge ◽

Ill Posed ◽

Using Data

Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.

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