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State of the art paper

Igor Mandric, Sergey Knyazev, Alex Zelikovsky
Summary: Genomic sequences are assembled into a variable, but large number of contigs that should be scaffolded (ordered and oriented) for facilitating comparative or functional analysis. Finding scaffolding is computationally challenging due to misassemblies, inconsistent coverage across the genome, and long repeats. An accurate assessment of scaffolding tools should take into account multiple locations of the same contig on the reference scaffolding rather than matching a repeat to a single best location...
March 14, 2018: Bioinformatics
Sara Moccia, Elena De Momi, Sara El Hadji, Leonardo S Mattos
BACKGROUND: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e...
May 2018: Computer Methods and Programs in Biomedicine
Shuchao Pang, Mehmet A Orgun, Zhezhou Yu
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images...
May 2018: Computer Methods and Programs in Biomedicine
Sara Moccia, Gabriele O Vanone, Elena De Momi, Andrea Laborai, Luca Guastini, Giorgio Peretti, Leonardo S Mattos
BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance...
May 2018: Computer Methods and Programs in Biomedicine
Piotr Chudzik, Somshubra Majumdar, Francesco Calivá, Bashir Al-Diri, Andrew Hunter
BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. METHODS: A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three...
May 2018: Computer Methods and Programs in Biomedicine
Long Chen, Wen Tang, Nigel W John, Tao Ruan Wan, Jian Jun Zhang
BACKGROUND AND OBJECTIVE: While Minimally Invasive Surgery (MIS) offers considerable benefits to patients, it also imposes big challenges on a surgeon's performance due to well-known issues and restrictions associated with the field of view (FOV), hand-eye misalignment and disorientation, as well as the lack of stereoscopic depth perception in monocular endoscopy. Augmented Reality (AR) technology can help to overcome these limitations by augmenting the real scene with annotations, labels, tumour measurements or even a 3D reconstruction of anatomy structures at the target surgical locations...
May 2018: Computer Methods and Programs in Biomedicine
Amit Acharyya, Pranit N Jadhav, Valentina Bono, Koushik Maharatna, Ganesh R Naik
BACKGROUND AND OBJECTIVE: EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment...
May 2018: Computer Methods and Programs in Biomedicine
Irene Fondón, Auxiliadora Sarmiento, Ana Isabel García, María Silvestre, Catarina Eloy, António Polónia, Paulo Aguiar
Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images...
March 8, 2018: Computers in Biology and Medicine
Han Liu, Xianchao Zhang, Xiaotong Zhang
Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good...
February 27, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Tianzhu Zhang, Si Liu, Changsheng Xu, Bin Liu, Ming-Hsuan Yang
In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses; 2) it can effectively handle large-scale variation via a particle sampling strategy; 3) it can efficiently maintain multiple modes in the posterior density using fewer particles than conventional particle filters, resulting in low computational cost; and 4) it can shepherd the sampled particles toward the modes of the target state distribution using a mixture of correlation filters, resulting in robust tracking performance...
June 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Margarita Villar, Lourdes Mateos-Hernandez, Jose de la Fuente
BACKGROUND: Why an autoimmune disease that is the main cause of the acute neuromuscular paralysis worldwide has not yet a well-characterized cause or an effective treatment? The existence of different clinical variants for the Guillain-Barré syndrome (GBS) coupled with the fact that a high number of pathogens can cause an infection that sometimes, but not always, precedes the development of the syndrome, confers a high degree of uncertainty for both prognosis and treatment. In the post-genomic era, the development of omics technologies for the high-throughput analysis of biological molecules is allowing the characterization of biological systems in a degree of depth unimaginable before...
March 14, 2018: Current Medicinal Chemistry
Victor Feliz Pedrinha, Juliana Melo da Silva Brandão, Oscar Faciola Pessoa, Patrícia de Almeida Rodrigues
Advances in endodontics have enabled the evolution of file manufacturing processes, improving performance beyond that of conventional files. In the present study, systems manufactured using state of the art methods and possessing special properties related to NiTi alloys ( i.e ., CM-Wire, M-Wire and R-Phase) were selected. The aim of this review was to provide a detailed analysis of the literature about the relationship between recently introduced NiTi files with different movement kinematics and shaping ability, apical extrusion of debris and dentin defects in root canal preparations...
2018: Open Dentistry Journal
Ivan Vujaklija, Vahid Shalchyan, Ernest N Kamavuako, Ning Jiang, Hamid R Marateb, Dario Farina
BACKGROUND: In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts' law approach. METHODS: Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics...
March 13, 2018: Journal of Neuroengineering and Rehabilitation
Giada Acciaroli, Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino
Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process...
March 13, 2018: Biosensors
Lei Xu, Guangmin Liang, Longjie Wang, Changrui Liao
Cancer is a serious health issue worldwide. Traditional treatment methods focus on killing cancer cells by using anticancer drugs or radiation therapy, but the cost of these methods is quite high, and in addition there are side effects. With the discovery of anticancer peptides, great progress has been made in cancer treatment. For the purpose of prompting the application of anticancer peptides in cancer treatment, it is necessary to use computational methods to identify anticancer peptides (ACPs). In this paper, we propose a sequence-based model for identifying ACPs (SAP)...
March 13, 2018: Genes
Shuying Huang, Jun Sun, Yong Yang, Yuming Fang, Pan Lin, Yue Que
Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the degradation of the observed image, which would lead to a poor SR performance. In this paper, we propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse representation. An EPS regularization term is designed based on gradient-domain-guided filtering to preserve image edges and reduce noise in the reconstructed image...
June 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Mengmeng Zhang, Wei Li, Qian Du
Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification...
June 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Hongyuan Zhu, Romain Vial, Shijian Lu, Xi Peng, Huazhu Fu, Yonghong Tian, Xianbin Cao
In this paper, we propose YoTube-a novel deep learning framework for generating action proposals in untrimmed videos, where each action proposal corresponds to a spatial-temporal tube that potentially locates one human action. Most of the existing works generate proposals by clustering low-level features or linking image proposals, which ignore the interplay between long-term temporal context and short-term cues. Different from these works, our method considers the interplay by designing a new recurrent YoTube detector and static YoTube detector...
June 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yawen Huang, Ling Shao, Alejandro F Frangi
Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions...
March 2018: IEEE Transactions on Medical Imaging
Morteza Modarresi Asem, Iman Sheikh Oveisi, Mona Janbozorgi
Retinal blood vessels indicate some serious health ramifications, such as cardiovascular disease and stroke. Thanks to modern imaging technology, high-resolution images provide detailed information to help analyze retinal vascular features before symptoms associated with such conditions fully develop. Additionally, these retinal images can be used by ophthalmologists to facilitate diagnosis and the procedures of eye surgery. A fuzzy noise reduction algorithm was employed to enhance color images corrupted by Gaussian noise...
July 2018: Journal of Medical Imaging
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