Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning in medical image analysis

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result using component-based voting based on the detected LV candidates, apex candidates, and base candidates.

Method and system for left ventricle detection in 2D magnetic resonance images

Fluoroscopic images contain useful information that is difficult to comprehend due to the collapse of the 3D information into 2D space. Extracting the informative layers and analyzing them separately could significantly improve the task of understanding the image content. Traditional Digital Subtraction Angiography (DSA) is not applicable for coronary angiography because of heart beat and breathing motion. In this work, we propose a layer extraction method for separating transparent motion layers in fluoroscopic image sequences, so that coronary tree can be better visualized.. The method is based on the fact that different anatomical structures possess different motion patterns, e.g. , heart is beating fast, while lung is breathing slower. A multiscale implementation is used to further improve the efficiency and accuracy. The proposed approach helps to enhance the visibility of the vessel tree, both visually and quantitatively.

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Coronary Tree Extraction Using Motion Layer Separation

A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.

Systems and methods for estimating physiological heart measurements from medical images and clinical data

A method for three-dimensional contour tracking includes building a plurality of shape models, building a plurality of appearance models, training a learning/classification algorithm using said shape models and appearance models, localizing a contour using the said learning/classification algorithm about an object in a digitized image, and tracking said contour along said object in 3D incorporating said learning/classification algorithm.

System and method for 3d contour tracking of anatomical structures

Many vision problems can be cast as optimizing the conditional probability density function p(C\I) where I is an image and C is a vector of model parameters describing the image. Ideally, the density function p(C\I) would be smooth and unimodal allowing local optimization techniques, such as gradient descent or simplex, to converge to an optimal solution quickly, while preserving significant nonlinearities of the model. We propose to learn a conditional probability density satisfying these desired properties for the given training data set. To do this, we formulate a novel regression problem that finds a function approximating the target density. Learning the regressor is challenging due to the high dimensionality of model parameters, C, and the complexity of relating the image and the model. Our approach makes two contributions. First, we take a multilevel refinement approach by learning a series of density functions, each of which guides the solution of optimization algorithms increasingly converging to the correct solution. Second, we propose a new data sampling algorithm that takes into account the gradient information of the target function. We have applied this learning approach to deformable shape segmentation and have achieved better accuracy than the previous methods.

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Dorin Comaniciu

Papers

Method and system for left ventricle detection in 2D magnetic resonance images

Coronary Tree Extraction Using Motion Layer Separation

Systems and methods for estimating physiological heart measurements from medical images and clinical data

System and method for 3d contour tracking of anatomical structures

Conditional density learning via regression with application to deformable shape segmentation