Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI

This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of y = Ax + b, named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

Detection of Brain Injury Using Different Soft Computing Techniques: A Survey

The detection of brain injury is one of the important and difficult task in the field of medicine. If the brain injuries are not detected in time, then it can cause serious problems in patients and sometimes can even lead to death. Traumatic brain injury (TBI) is one of the major causes of mortality and poor quality of life among the survivors. Various imaging techniques are available for taking the images of the brain so that the injuries can be detected. Magnetic resonance imaging (MRI) is one of the common medical imaging technique used for the delineation of soft tissues such as that of the brain. This paper analyses few of the methods and their performances that have been proposed for the detection of the brain injury. In these methods different soft computing techniques such as artificial neural networks (ANN), k nearest neighbor (k-NN), support vector machine (SVM), Parzan window, etc. were used for the classification of abnormal and normal brain images. Before classification feature extraction and reduction were done using the methods such as DWT, GLCM, PCA, etc. DOI: 10.17762/ijritcc2321-8169.15030

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Reasoning with uncertainty using Nilsson's probabilistic logic and the maximum entropy formalism

An expert system must reason with certain and uncertain information. This thesis is concerned with the process of Reasoning with Uncertainty. Nilsson's elegant model of "Probabilistic Logic" has been chosen as the framework for this investigation, and the information theoretical aspect of the maximum entropy formalism as the inference engine. These two formalisms, although semantically compelling, offer major complexity problems to the implementor. Probabilistic Logic models the complete uncertainty space, and the maximum entropy formalism finds the least commitment probability distribution within the uncertainty space. The main finding in this thesis is that Nilsson's Probabilistic Logic can be successfully developed beyond the structure proposed by Nilsson. Some deficiencies in Nilsson's model have been uncovered in the area of probabilistic representation, making Probabilistic Logic less powerful than Bayesian Inference techniques. These deficiencies are examined and a new model of entailment is presented which overcomes these problems, allowing Probabilistic Logic the full representational power of Bayesian Inferencing. The new model also preserves an important extension which Nilsson's Probabilistic Logic has over Bayesian Inference: the ability to use uncertain evidence. Traditionally, the probabilistic, solution proposed by the maximum entropy formalism is arrived at by solving non-linear simultaneous equations for the aggregate factors of the non- linear terms. In the new model the maximum entropy algorithms are shown to have the highly desirable property of tractability. Although these problems have been solved for probabilistic entailment the problems of complexity are still prevalent in large databases of expert rules. This thesis also considers the use of heuristics and meta level reasoning in a complex knowledge base. Finally, a description of an expert system using these techniques is given

Context–aware Learning for Generative Models

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maximization (EM) improves over the regular unsupervised case and can approach the performances of supervised learning, despite the absence of any explicit ground-truth data labeling. By direct application of the missing information principle (MIP), the algorithms' performances are proven to range between the conventional supervised and unsupervised MLE extremities proportionally to the information content of the contextual assistance provided. The acquired benefits regard higher estimation precision, smaller standard errors, faster convergence rates, and improved classification accuracy or regression fitness shown in various scenarios while also highlighting important properties and differences among the outlined situations. Applicability is showcased with three real-world unsupervised classification scenarios employing Gaussian mixture models. Importantly, we exemplify the natural extension of this methodology to any type of generative model by deriving an equivalent context-aware algorithm for variational autoencoders (VAs), thus broadening the spectrum of applicability to unsupervised deep learning with artificial neural networks. The latter is contrasted with a neural-symbolic algorithm exploiting side information

Classification and Decision Making of Medical Infrared Thermal Images

Medical infrared thermal imaging (MITI) is a technique that allows safe and non-invasive recording of skin surface temperature distribution. The images gained provide underlining physiological information on the blood flow, vasoconstriction/vasodilatation, inflammation, transpiration or other processes that can contribute to skin temperature. This medical imaging modality has been available for nearly six decades and has proved to be useful for vascular, neurological and musculoskeletal conditions. Since the recordings are digital, in the form of a matrix of numbers (image), it can be computationally analyzed by a specialist mainly performing processing and analysis operations manually supported by proprietary software solutions. This limits the number of images that can be processed, making difficult for knowledge to evolve, expertise to develop and information to be shared. This chapter aims to disclose the medical imaging method, along with its particularities, principles, applications, advantages and disadvantages. The chapter introduces all available classification and decision making methods that can be employed using digital information, together with a literature review of their operation in the biomedical applications of infrared thermal imaging.info:eu-repo/semantics/publishedVersio