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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： M. A. Khorsandi N. Karimi S. Samavi M. Hajabdollahi S. M.R. Soroushmehr K. Ward K. Najarían Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Michigan Center for Integrative Research in Critical Care Emergency Medicine Department University of Michigan Ann Arbor U.S.A. Department of Computational Medicine and Bioinformatics Department of Emergency Medicine Michigan Center for Integrative Research in Critical Care University of Michigan Ann Arbor U.S.A.

ISBN: (纸本)9781457702198

Wireless capsule endoscopy is a new technology in the realm of telemedicine that has many advantages over the traditional endoscopy systems. Transmitted images should help diagnosis of diseases of the gastrointestinal tract. Two important technical challenges for the manufacturers of these capsules are power consumption and size of the circuitry. Also, the system must be fast enough for real-time processing of image or video data. To solve this problem, many hardware designs have been proposed for implementation of the image processing unit. In this paper we propose an architecture that could be used for the assessment of endoscopy images. The assessment allows avoidance of transmission of medically useless images. Hence, volume of data is reduced for more efficient transmission of images by the endoscopy capsule. This is done by color space conversion and moment calculation of images captured by the capsule. The inputs of the proposed architecture are RGB image frames and the outputs are images with converted colors and calculated image moments. Experimental results indicate that the proposed architecture has low complexity and is appropriate for a real-time application.

关键词： Image color analysis Hardware Computer architecture Endoscopes Power demand Pipelines Wireless sensor networks

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Automatic segmentation of multimodal brain tumor images based on classification of super-voxels

Automatic segmentation of multimodal brain tumor images base...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： M. Kadkhodaei S. Samavi N. Karimi H. Mohaghegh S. M. R. Soroushmehr K. Ward A. All K. Najarían Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Johns Hopkins School of Medicine Baltimore MD USA Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor U.S.A.

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

关键词： Tumors Image segmentation Feature extraction Image edge detection Brain Clustering algorithms Classification algorithms

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Vessel extraction in X-ray angiograms using deep learning

Vessel extraction in X-ray angiograms using deep learning

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： E. Nasr-Esfahani S. Samavi N. Karimi S.M.R. Soroushmehr K. Ward M.H. Jafari B. Felfeliyan B. Nallamothu K. Najarian Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Emergency Medicine University of Michigan Ann Arbor U.S.A. Emergency Medicine Department University of Michigan Ann Arbor U.S.A. Department of Internal Medicine University of Michigan Ann Arbor U.S.A. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor U.S.A.

ISBN: (纸本)9781457702198

Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images. Initially, an input angiogram is preprocessed to enhance its contrast. Afterward, the image is evaluated using patches of pixels and the network determines the vessel and background regions. A set of 1,040,000 patches is used in order to train the deep CNN. Experimental results on angiography images of a dataset show that our proposed method has a superior performance in extraction of vessel regions.

关键词： Feature extraction Image segmentation Arteries Training Angiography Filter banks Transforms

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CT Image Segmentation in Traumatic Brain Injury

CT Image Segmentation in Traumatic Brain Injury

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Annual International conference of the ieee Engineering in Medicine and biology Society

作者： S.M.R. Soroushmehr A. Bafna S. Schlosser K. Ward H. Derksen K. Najarian the University of Michigan Center for Integrative Research in Critical Care (MCIRCC) and the Emergency Medicine Department University of Michigan Ann Arbor MI the Electrical Engineering and Computer Science Department University of Michigan Ann Arbor MI the NovoDynamics Inc. Ann Arbor MI USA the University of Michigan Center for Integrative Research in Critical Care (MCIRCC) and the department of Mathematics University of Michigan Ann Arbor Michigan Ann Arbor USA the department of Computational Medicine and Bioinformatics as well as University of Michigan Center for Integrative Research in Critical Care (MCIRCC) and the Emergency Medicine Department University of Michigan Ann Arbor MI

ISBN: (纸本)9781424492695

Traumatic brain injury (TBI) is a major cause of disability and death. Speed and accuracy are vital in diagnosing TBI for which computer-aided imaging analysis may speedup and improve the efficiency of diagnosis and help reduce mortality, long-term complications, and the associated costs. However, developing such a system is challenging due to some factors such as the inherent noise associated with obtaining the images, artifacts and quality of the images. An automated system that can preliminary identify, localize and quantify the imaging features of TBI would be beneficial in guiding real-time clinical diagnosis as well as for quality assurance. In this paper we propose an automated system to segment the hematoma region from CT images. The proposed method first performs denoising and image enhancement and then by developing a Gaussian mixture model, segmentation is carried out. We show the performance of the system by comparing the results with ground truth generated by specialists.

关键词： CT Image Segmentation Traumatic Brain Injury TBI

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Mitochondrial protein cleavage site predictor

Mitochondrial protein cleavage site predictor

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ieee International conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Yoshinori Fukasawa Raymond Wan Szu-Chin Fu Junko Tsuji Noriyuki Sakiyama Kenichiro Imai Paul Horton Tokyo Daigaku Bunkyo-ku Tokyo JP Graduate School of Frontier Sciences University of Tokyo Japan Computational Biology Research Center Japan

Summary form only given. We have constructed a new tool to predict the cleavage sites of mitochondrial proteins. Trained on newer data, in preliminary results our tool compares favorably with the industry standard tool TargetP. In most eukaryotic cells, over 1000 nuclear-encoded proteins are translocated into the mitochondria to perform their function. Roughly half of these have an N-terminal sorting signal known as the MTS (Matrix Targeting Signal) and most of those are cleaved after mitochondrial import by MPP (Mitochondrial Protein Peptidase) and other peptidases. The position of these cleavage sites is necessary to know the amino acid sequence of the mature protein. TargetP is a classic tool for predicting MPP cleavage sites. However, recently large-scale experiments in yeast gave us an opportunity to create a new classifier based on much more data. Ultimately we hope to predict all of the cleavage events experienced by proteins as they mature in the mitochondria, but in our poster we will describe the framework shown in figure 2, encompassing the peptidases MPP, Octl, and Icp55. MPP typically cleaves one residue after an arginine. Subsequently, some proteins are further cleaved by Octl (cleaving eight residues) or by Icp55 (cleaving one residue). Figure 1 shows the sequence logos for the inferred cleavage sites of each peptidase and an extra group of currently unexplained data. As in Vogtle et al, we have inferred some intermediate sites and the identity of the peptidases based on the position of arginines. Currently we have had the most success with predicting MPP sites. Our first attempt uses a profile HMM (Hidden Markov Model) as implemented by the HMMER2 package (***/). Preliminary cross-validation results suggest that our new predictor outperforms TargetP for this task. We are also exploring other techniques including SVM classifiers and Dynamic Bayesian networks.

关键词： Argon Hidden Markov models

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AI-Driven Decision Support System for Heart Failure Diagnosis: INTELHEART Approach Towards Personalized Treatment Strategies

AI-Driven Decision Support System for Heart Failure Diagnosi...

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Smiljana Tomasevic Andjela Blagojevic Tijana Geroski Gordana Jovicic Bogdan Milicevic Momcilo Prodanovic Ilija Kamenko Bojana Bajic Stefan Simovic Goran Davidovic Dragana Ignjatovic Ristic Andrej Preveden Lazar Velicki Arsen Ristic Svetlana Apostolovic Edin Dolicanin Nenad Filipovic Faculty of Engineering University of Kragujevac Serbia Institute for Information Technologies Kragujevac Serbia Institute for Artificial Intelligence Research and Development of Serbia Novi Sad Serbia Faculty of Medical Sciences University of Kragujevac Serbia Faculty of Medicine University of Novi Sad Serbia Faculty of Medicine (School of Medicine) University of Belgrade Serbia Faculty of Medicine University of Nis Serbia State University of Novi Pazar Serbia

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure is recognized as a modern epidemic and despite advances in therapy and research, heart failure still carries an ominous prognosis and a significant socioeconomic burden. The main aim of this paper is to demonstrate how novel Decision Support System (DSS) and computational platform like INTELHEART can transform the future of healthcare and early diagnosis of heart failure. The main idea is integration of patient-specific data (i.e. demographic and physical characteristics, medical history, symptoms and signs) and results obtained using existing and novel diagnostic technologies into the cloud environment. Data will be used by different tools for machine learning and computational modelling, developing virtual patient population. Moreover, voice as a biomarker will be collected among participating patients, in order to create a VoiceHeart mobile app. INTELHEART represents a transformative advancement in heart failure care, aiming to make treatment more personalized, and proactive. This initiative centers on precision medicine, using AI-driven analysis and a powerful DSS alongside the cloud-based platform and VoiceHeart mobile app to assist both clinicians and patients. Additionally, it incorporates assessments of psychological resilience and emotional well-being, addressing the oftenoverlooked mental health factors essential to comprehensive heart failure management.

关键词： Decision support systems Cloud computing Precision medicine Transforms Mobile applications Cardiovascular diseases Socioeconomics Prognostics and health management Medical diagnostic imaging Resilience

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Frequent Closed Itemset Mining with Privacy Preserving for Distributed Databases

Frequent Closed Itemset Mining with Privacy Preserving for D...

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ieee International conference on Data Mining Workshops (ICDM Workshops)

作者： Shin-ya Kuno Koichiro Doi Akihiro Yamamoto Nomura Research Institute Limited Japan Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan Department of Computational Biology Department of Frontier Sciences University of Tokyo Chiba Japan

In the present paper we introduce closed item sets into frequent item set mining from horizontally-partitioned transaction databases with preserving privacy. Closed item sets were originally from the research area of Formal Concept Analysis, and it is shown that even if results of frequent item set mining are restricted to closed item sets, all frequent item sets can be recovered from the results. This property suggests that using closed item sets would contribute to decreasing the cost of communication among distributed sites where a piece of horizontally-partitioned database is stored. We present a mining procedure revising and amalgamating two previous works: one is for mining closed item sets from horizontally-partitioned databases, and the other is for privacy preserving mining of item sets from such databases. We analyze the procedure on both of the viewpoint of communication cost and that of security. We also show results of some experimental practice of applying the procedure to a well-known dataset.

关键词： Itemsets Distributed databases Data privacy Protocols Silicon

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Predicting Protein-Protein Interactions in E. Coli Using Machine Learning Methods

Predicting Protein-Protein Interactions in E. Coli Using Mac...

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Proceedings of the 46th ieee conference on Decision and Control, Pages 1839-2300

作者： Kshama Goyal M. Vidyasagar Tata Consultancy Services Limited Hyderabad India

ISBN: (纸本)9781424414970;1424414970

The multitude of protein-protein interactions allows an organism to function and maintain cellular homeostasis. Several high-throughput techniques are employed to decipher complete interaction network of a cell and to understand its biology. However, experimental techniques are flawed by the large amount of noise and are limited by the lack of coverage. computational techniques are therefore sought to predict genome-wide protein-protein interactions. In silico approaches mainly use one or the other genome-context methods toidentify the interacting protein pairs. Machine learning algorithms trained on physicochemical characteristics of the proteins have also been used to determine interacting partners. In this work, we have used combined genome context methods as data features to train machine learning algorithms. We observed through several numerical experiments that NN performs better than SVMs on a known dataset. We also aim to predict genome wide protein interaction network for different organism using the best model and efficient algorithm.

关键词： Protein-Protein Interaction Escherichia coli Machine learning algorithms Protein Interaction Maps Decryption Organism efficient algorithm Artificial intelligence Machine Learning Cells

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Getting 'phi psi chi al' with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles

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bioinformatics 2023年第Supplement_1期39卷 I357-I367页

作者： Amarasinghe, Piyumi R. Allison, Lloyd Stuckey, Peter J. de la Banda, Maria Garcia Lesk, Arthur M. Konagurthu, Arun S. Monash Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Clayton Vic 3800 Australia OPTIMA ARC Ind Training & Transformat Ctr Carlton Vic 3053 Australia Penn State Univ Dept Biochem & Mol Biol University Pk PA 16802 USA

The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles () of any amino acid by a mixture of a product of von Mises probability distributions. This mixture model maps any vector of dihedral angles to a point on a multi-dimensional torus. The continuous space it uses to specify the dihedral angles provides an alternative to the commonly used rotamer libraries. These rotamer libraries discretize the space of dihedral angles into coarse angular bins, and cluster combinations of sidechain dihedral angles () as a function of backbone conformations. A `good' model is one that is both concise and explains (compresses) observed data. Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (phi psi chi al).

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Evolvable hardware: on the automatic synthesis of analog control systems

Evolvable hardware: on the automatic synthesis of analog con...

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ieee conference on Aerospace

作者： R.S. Zebulum M. Vellasco M.A. Pacheco H.T. Sinohara ICA: Applied Computational Intelligence Laboratory Electric Engineering Department Catholic University of Rio de Janeiro Rio de Janeiro Brazil

The automatic design of analog control circuits is the focus of this work. Particularly, we apply Evolutionary Computation to carry out the process of circuit synthesis. Evolutionary Computation encompasses a set of search algorithms, called Evolutionary Algorithms (EAs), which borrow from biological evolution their main principles. This particular area of research, where EAs are applied to electronic circuit synthesis, receives the name of Evolvable Hardware (EHW). Our technique differs from conventional approaches in that it considers specific circuit's implementation requirements, such as power dissipation, intrinsic noise and fault tolerance, in addition to standard specifications for control systems, such as rise time and over-signal. Our results compare well with conventionally designed controllers presented in the literature, exhibiting a lower degree of oscillation, using lesser components, and achieving better fault tolerance properties.

关键词： Hardware Automatic control Circuit synthesis Evolutionary computation Evolution (biology) Electronic circuits Control system synthesis Power dissipation Circuit noise Fault tolerant systems

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