Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.

Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement.

Multimodal Image Registration Using a Viscous Fluid Model with the Bhattacharyya Distance.

Image registration is an elementary task in medical image processing and analysis, which can be divided into monomodal and multimodal. Direct 3D multimodal registration in volumetric medical images can provide more insight into the interpretation of subsequent image processing applications than 2D methods. This paper is dedicated to the development of a 3D multimodal image registration algorithm based on a viscous fluid model associated with the Bhattacharyya distance.

Automated Stroke Lesion Segmentation in Rat Brain MR Images Using an Encoder-Decoder Framework.

Stroke is a leading cause of serious long-term disability and the major cause of mortality worldwide. Experimental ischemic stroke models play an important role in realizing the mechanism of cerebral ischemia and evaluating the development of pathological extent. An accurate and reliable image segmentation tool to automatically identify the stroke lesion is important in the subsequent processes.

RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net.

Background: Experimental ischemic stroke models play a fundamental role in interpreting the mechanism of cerebral ischemia and appraising the development of pathological extent. An accurate and automatic skull stripping tool for rat brain image volumes with magnetic resonance imaging (MRI) are crucial in experimental stroke analysis. Due to the deficiency of reliable rat brain segmentation methods and motivated by the demand for preclinical studies, this paper develops a new skull stripping algorithm to extract the rat brain region in MR images after stroke, which is named Rat U-Net (RU-Net).

Segmentation of Rat Brains and Cerebral Hemispheres in Triphenyltetrazolium Chloride-Stained Images after Stroke.

Ischemic stroke is one of the leading causes of death among the aged population in the world. Experimental stroke models with rodents play a fundamental role in the investigation of the mechanism and impairment of cerebral ischemia. For its celerity and veracity, the 2,3,5-triphenyltetrazolium chloride (TTC) staining of rat brains has been extensively adopted to visualize the infarction, which is subsequently photographed for further processing.

Automatic brain extraction and hemisphere segmentation in rat brain MR images after stroke using deformable models.

Purpose: Experimental ischemic stroke models play an essential role in understanding the mechanisms of cerebral ischemia and evaluating the development of pathological extent. An important precursor to the investigation of ischemic strokes associated with rodents is the brain extraction and hemisphere segmentation in rat brain diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) images. Accurate and reliable image segmentation tools for extracting the rat brain and hemispheres in the MR images are critical in subsequent processes, such as lesion identification and injury analysis.

An automatic restoration framework based on GPU-accelerated collateral filtering in brain MR images.

Background: Image restoration is one of the fundamental and essential tasks within image processing. In medical imaging, developing an effective algorithm that can automatically remove random noise in brain magnetic resonance (MR) images is challenging. The collateral filter has been shown a more powerful algorithm than many existing methods.

An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images.

Bilateral filters have been extensively utilized in a number of image denoising applications such as segmentation, registration, and tissue classification. However, it requires burdensome adjustments of the filter parameters to achieve the best performance for each individual image. To address this problem, this paper proposes a computer-aided parameter decision system based on image texture features associated with neural networks.

Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration.

Objective: Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration.

Methods: Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images.

Brain segmentation in MR images using a texture-based classifier associated with mathematical morphology.

Skull stripping, which refers to the segmentation of brain tissue from non-brain tissue, has been challenging due to the ramification of the human brain structures and volatile parameters in the magnetic resonance imaging (MRI) procedures. It has been one of the most critical preprocessing steps in medical image analysis. We propose a hybrid skull stripping algorithm that is based on texture feature analysis, fuzzy possibilistic c-means (FPCM), and morphological operations.

CUDA-based acceleration and BPN-assisted automation of bilateral filtering for brain MR image restoration.

Purpose: Bilateral filters have been substantially exploited in numerous magnetic resonance (MR) image restoration applications for decades. Due to the deficiency of theoretical basis on the filter parameter setting, empirical manipulation with fixed values and noise variance-related adjustments has generally been employed. The outcome of these strategies is usually sensitive to the variation of the brain structures and not all the three parameter values are optimal.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures.

Adaptive registration of magnetic resonance images based on a viscous fluid model.

This paper develops a new viscous fluid registration algorithm that makes use of a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid governed by the nonlinear Navier-Stokes partial differential equation (PDE) that varies in both temporal and spatial domains. We replace the pressure term with an image-based body force to guide the transformation that is weighted by the mutual information between the template and reference images.

Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification.

Skull-stripping in magnetic resonance (MR) images is one of the most important preprocessing steps in medical image analysis. We propose a hybrid skull-stripping algorithm based on an adaptive balloon snake (ABS) model. The proposed framework consists of two phases: first, the fuzzy possibilistic c-means (FPCM) is used for pixel clustering, which provides a labeled image associated with a clean and clear brain boundary.

Active shape modeling with electric flows.

Physics-based particle systems are an effective tool for shape modeling. Also, there has been much interest in the study of shape modeling using deformable contour approaches. In this paper, we describe a new deformable model with electric flows based upon computer simulations of a number of charged particles embedded in an electrostatic system.

Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling.

Purpose: Three-dimensional rotational angiography (3DRA) is an evolving imaging procedure from traditional digital subtraction angiography and is gaining much interest for detecting intracranial aneurysms. Computational fluid dynamics (CFD) modeling plays an important role in understanding the biomechanical properties and in facilitating the prediction of aneurysm rupture. A successful computational study relies on an accurate description of the vascular geometry that is obtained from volumetric images.

Performance measure characterization for evaluating neuroimage segmentation algorithms.

Characterizing the performance of segmentation algorithms in brain images has been a persistent challenge due to the complexity of neuroanatomical structures, the quality of imagery and the requirement of accurate segmentation. There has been much interest in using the Jaccard and Dice similarity coefficients associated with Sensitivity and Specificity for evaluating the performance of segmentation algorithms. This paper addresses the essential characteristics of the fundamental performance measure coefficients adopted in evaluation frameworks.

Computer simulations of isolated conductors in electrostatic equilibrium.

A computer simulation model is introduced to study the characteristics of isolated conductors in electrostatic equilibrium. Drawing an analogy between electrons and how they move to the surface of isolated conductors, we randomly initialize a large number of particles inside a small region at the center of simulated conductors and advance them according to their forces of repulsion. By use of optimized numerical techniques of the finite-size particle method associated with Poisson's equation, the particles are quickly advanced using a fast Fourier transform and their charge is efficiently shared using the clouds-in-cells method.

An electrostatic deformable model for medical image segmentation.

A new deformable model, the charged fluid model (CFM), that uses the simulation of charged elements was used to segment medical images. Poisson's equation was used to guide the evolution of the CFM in two steps. In the first step, the elements of the charged fluid were distributed along the propagating interface until electrostatic equilibrium was achieved.

Segmentation of brain MR images using a charged fluid model.

In this paper, we developed a new deformable model, the charged fluid model (CFM), that uses the simulation of a charged fluid to segment anatomic structures in magnetic resonance (MR) images of the brain. Conceptually, the charged fluid behaves like a liquid such that it flows through and around different obstacles. The simulation evolves in two steps governed by Poisson's equation.