Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost

In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include additional information in the form of constraints to the clustering process, present better visual segmentation

A method for the theoretical estimation of the MTF of columnar phosphors with a homogeneous part at the end used in X-ray imaging has been developed. This method considers the light transport inside the scintillator through an analytical modelling, the optical photon beams distribution on the scintillator-optical sensor interface, and uses the definition of the PSF and a Gauss fitted LSF to estimate

Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates

To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment

Classification of cancerous genes from microarray data is an important research area in bioinformatics. Large amount of microarray data are available, but it is very costly to label them. This paper proposes an active learning model, a semi-supervised classification approach, to label the microarray data using which predictions can be made even with lesser amount of labeled data. Initially, a pool

An efficient and novel modeling approach is proposed in this paper for identifying proteins or genes involved in melanoma skin cancer. Two types of classifiers are modeled, based on the chemical structure and hydropathy property of amino acids. These classifiers are further implemented using NI LabVIEW–based hardware kit to observe the real-time response for proper diagnosis. The phase responses, pole-zero

Computer-aided diagnosis (CAD) is widely used for early diagnosis of breast cancer. The commonly used morphological feature (MF), dynamic feature (DF), and texture feature (TF) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been proved very valuable and are studied in this paper. However, previous studies ignored the prior knowledge that most of the benign lesions have

Image fusion (IF) attracts the researchers in the areas of the medical industry as valuable information could be afforded through the fusion of images that enable the clinical decisions to remain effective. With the aim to render an effective image fusion, this paper concentrates on the Bayesian fusion approach, which is tuned optimally using the proposed Fractional Bird Swarm Optimization (Fractional-BSA)

Proper alignment of acetabular implantation is necessary for good patient outcomes and preventing complications or additional surgeries in total hip arthroplasty (THA). Rotation of the pelvis in lateral decubitus (LD) is typically not accounted for as surgeons use the surgery table as a reference plane to align implants. This study compared four techniques to measure 3D pelvic rotations in LD position

The upper airway (UA) is in general thicker and narrower in obstructive sleep apnea (OSA) population than in normal. Additionally, the UA changes during sleep are much more in the OSA population. The UA changes can alter the tracheal breathing sound (TBS) characteristics. Therefore, we hypothesize the TBS changes from wakefulness to sleep are significantly correlated to the OSA severity; thus, they

Human skull is an important body structure for jaw movement and facial mimic simulations. Surface head can be reconstructed using 3D scanners in a straightforward way. However, internal skull is challenging to be generated when only external information is available. Very few studies in the literature focused on the skull generation from outside head information, especially in a subject-specific manner

Capsule endoscopy is a new type of technology in the diagnosis and treatment of digestive diseases, with painless and low invasive features. However, current capsule robots have many problems, such as over-sized, single function and lack of active locomotion control. This study proposed and designed a new wireless modular capsule robotic system in pipe. The modular capsule robots could move forward

Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and amalgamate them into a uniform format for easier access by the end-user like biologist, pharmacist, and data scientist. The proposed integrated biomedical

Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy

During the process of percutaneous puncture, a medical needle with a beveled tip is usually inserted into a multilayered soft tissue, which contains a muscle layer, before it reaches the target. The robot-assisted needle insertion technology will help brachytherapists to perform high-accuracy seed implantation. To guide the automatic needle insertion, a mechanics-based model to simulate needle deflection

In this study, we propose an integrated tooth segmentation and gingival tissue deformation simulation framework used to design and evaluate the orthodontic treatment plan especially with invisible aligners. Firstly, the bio-characteristics information of the digital impression is analyzed quantitatively and demonstrated visually. With the derived information, the transitional regions of tooth-tooth

At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipment. To improve the efficiency, accuracy, and applicability of mortality prediction in the remote areas, a novel mortality prediction method based on machine

Prosthetic feet have generally been designed experimentally by adopting a trial-and-error technique. The objective of this research is to introduce a novel numerical approach for the a priori evaluation of the roll-over shape (ROS) of a prosthetic foot for application in its systematic design and development. The ROS was achieved numerically by employing a non-linear finite element model incorporating

Pathogenesis of different diseases showed that some of them, especially thalassemia (T) and rheumatoid arthritis (RA) have an implicit association with oxidative stress and altered levels of reactive oxygen species (ROS). Introducing ROS level and the balance between ROS and antioxidants as essential metrics, an attempt was made to classify T and RA from normal individuals (treated as controls)(C)

The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual segmentation cannot be achieved within a sensible time that restricts the usage of accurate quantitative measurement in clinical practice. Therefore,

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samples are generally scarce and expensive to collect, while unlabeled samples are considered to be abundant in real applications. Although the semi-supervised learning (SSL) allows us to utilize both labeled and unlabeled data to improve the classification

The fast hybrid operator splitting (HOS) and stable uniformization (UNI) methods have been proposed to save computation cost and enhance stability for Markov chain model in cardiac cell simulations. Moreover, Chen-Chen-Luo’s quadratic adaptive algorithm (CCL) combined with HOS or UNI was used to improve the tradeoff between speedup and stability, but without considering accuracy. To compromise among

In this study, we propose a computational characterization technique for obtaining the material properties of axons and extracellular matrix (ECM) in human brain white matter. To account for the dynamic behavior of the brain tissue, data from time-dependent relaxation tests of human brain white matter in different strain rates are extracted and formulated by a visco-hyperelastic constitutive model

Pulmonary diseases and injury lead to structural and functional changes in the lung parenchyma and airways, often resulting in measurable sound transmission changes on the chest wall surface. Additionally, noninvasive imaging of externally driven mechanical wave motion in the chest (e.g., using magnetic resonance elastography) can provide information about lung stiffness and other structural property

Lung diffusion-weighted magnetic resonance imaging (DWI) has shown a promising value in lung lesion detection, diagnosis, differentiation, and staging. However, the respiratory and cardiac motion, blood flow, and lung hysteresis may contribute to the blurring, resulting in unclear lung images. The image blurring could adversely affect diagnosis performance. The purpose of this study is to reduce the

Population registration has been proposed for normalizing a large group of images into a common space, which is important in many clinical and research studies, such as brain development, aging, and atlas construction. Different from pairwise registration problem that aligns the target image to the reference directly, determining the reference or the hidden common space with the least bias is important

Conduct disorder (CD) is an important mental health problem in childhood and adolescence. There is presently a trend of revealing neural mechanisms using measures of brain networks. This study goes further by presenting a classification scheme to distinguish subjects with CD from typically developing healthy subjects based on measures of small-world networks. In this study, small-world networks were

Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumor tissue to characterize the tumor class. However, other tissues are also helpful for providing the references. In this paper, a multi-target semantic segmentation approach is proposed based on the fully convolutional network for segmenting the breast

Robot-assisted technologies are overcoming the limitations of the current approaches for transoral surgeries, which are suffering from limited vision and workspace. As a result, we develop a novel teleoperated parallel continuum robot with variable stiffness for collision avoidance. This paper focuses on the feasibility study on a cadaveric model for the robotic system as a first trial. We introduce

We purpose a novel method that combines modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) for classifying normal and abnormal heart sounds. A hidden Markov model is used to find the position of each cardiac cycle in the heart sound signal and determine the exact position of the four periods of S1, S2, systole, and diastole. Then the one-dimensional cardiac cycle

Algorithms are an essential part of radiation therapy planning, which includes three optimizations problems: beam angle configuration, fluence map, and realization. This study addresses the third one, also called the leaf sequencing problem, which arises for each chosen irradiation angle, given the optimized fluence map. It consists in defining a sequence of configurations of a device (called multileaf