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Although image segmentation technology has achieved rapid development, threshold method is still an indispensable part in many practical applications. The most advanced methods do not perform well in the segmentation of many different types of images. Therefore, it is expected that the optimal segmentation method can be obtained for images with different modalities. In this paper, a robust threshold...
Although 3D ultrasound plays an increasingly important role, 2D echocardiography remains the main clinical imaging modality for cardiac function assessment in daily practice. This requires precise delineation of the myocardium at end diastole (ED) and systole (ES). Because of intrinsic high variability in image quality, manual interactions are still needed. In this study, we investigate a machine...
Cardiac function assessment is a critical step in cardiology and 3D ultrasound plays an increasingly important role. Automatic left ventricular (LV) segmentation remains however challenging particularly in the presence of artifacts and for images with low contrast-to-noise ratio (CNR). It is thus crucial to give segmentation tools prior information on the LV shape in order to fill in the gaps when...
The automated segmentation of cells in microscopic images is an open research problem that has important implications for studies of the developmental and cancer processes based on in vitro models. In this paper, we present the approach for segmentation of the DIC images of cultured cells using G-neighbor smoothing followed by Kauwahara filtering and local standard deviation approach for boundary...
This paper proposes a methodology to be used in the segmentation of infrared thermography images for the detection of bearing faults in induction motors. The proposed methodology can be a helpful tool for preventive and predictive maintenance of the induction motor. This methodology is based on manual threshold image processing to obtain a segmentation of an infrared thermal image, which is used for...
The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis...
Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background...
Features of sea ice is often not obvious, and to delineate feature areas manually would limit the effect of MCC method for tracking sea ice drift. This study focuses on automatic selection of feature regions. First, an object-oriented segmentation method, which is based on the level iterative merging algorithm, is used for the sea ice areas. Then the corner points are obtained as the candidate feature...
Massive traffic scene data for algorithm research and model training is the fundamental for self-driving car technology development. In the procedure of scene image labeling, the most accurate method is manual annotation, but with the increasing of the amount of image data, artificial annotation method becomes infeasible due to its disadvantages of vast cost, inefficiency and subjective deviation...
Convolutional Neural Network(CNN) based semantic segmentation require extensive pixel level manual annotation which is daunting for large microscopic images. The paper is aimed towards mitigating this labeling effort by leveraging the recent concept of generative adversarial network(GAN) wherein a generator maps latent noise space to realistic images while a discriminator differentiates between samples...
Deaf-mute communities around the world experience a need in effective human-robot interaction system that would act as an interpreter in public places such as banks, hospitals, or police stations. The focus of this work is to address the challenges presented to hearing-impaired people by developing an interpreting robotic system required for effective communication in public places. To this end, we...
Fluorescence in situ hybridization (FISH) is a technique that prepares acceptable results for molecular imaging biomarkers to precisely and dependably detect and diagnose disorders which are sign of cancers. Since contemporary manual FISH signal analysis is low-effective and inconsistent, it is an attractive research area to develop automated FISH image scanning systems and computer-aided diagnosis...
Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points...
Hippocampal shrinkage is a main biomarker for the detection of Alzheimer's disease and Temporal lobe Epilepsy (TLE). Mostly, developing methods for the hippocampus segmentation are unable to initialize automatically due to its low contrast boundary and uncertain position with respect to the wide range of human brain size. This paper will describe how to reduce the search area in brain MRI to determine...
Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images provides a basis for obtaining morphometric data of articular cartilages for investigation of pathoanatomical conditions such as osteoarthritis. In this paper, we present an automated MR-based cartilage segmentation method using an ensemble of neural networks for the individual femoral and acetabular cartilage plates...
This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders...
Segmentation of the developing cortical plate from MRI data of the post-mortem fetal brain is highly challenging due to partial volume effects, low contrast, and heterogeneous maturation caused by ongoing myelination processes. We present a new atlas-free method that segments the inner and outer boundaries of the cortical plate in fetal brains by exploiting diffusion-weighted imaging cues and using...
Artery-vein classification on pulmonary computed tomography (CT) images is becoming of high interest in the scientific community due to the prevalence of pulmonary vascular disease that affects arteries and veins through different mechanisms. In this work, we present a novel approach to automatically segment and classify vessels from chest CT images. We use a scale-space particle segmentation to isolate...
The evolutionary success of ants and other social insects is considered to be intrinsically linked to division of labor and emergent collective intelligence. The role of the brains of individual ants in generating these processes, however, is poorly understood. One genus of ant of special interest is Pheidole, which includes more than a thousand species, most of which are dimorphic, i.e. their colonies...
Automatic identification of side branch and main vascular measurements in IVOCT images take critical roles in pre-interventional decision making for coronary artery disease treatment. Very little works have been presented on these tasks. In this paper, we proposed a novel side branch identification algorithm which utilizes a newly defined global curvature feature to identify the ostium of side branch...
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