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If you give someone a wallet or. Ornamental plants with round leaves inside the house are signs of good luck, while keeping vines that grow downward are bad luck. To bring good luck to a child, its first extracted milk tooth is hidden under the roof. Good Luck Frog - The Frog as a Lucky Symbol I wondered if it was because they lacked money to buy a regular stone or was it because they were.
Ben Eastaugh and Chris Sternal-Johnson. In this lesson we gonna talk about Russian Superstitions. Giving an empty wallet can give the recipient bad. Merchants should sell their wares at a low price during New Year to attract more business. Doors erected on the left side of the house and stairs that turn to the left will encourage infidelity. Plants will wither and trees will bear sour fruits if touched by a pregnant woman. Showering the rooms of a new house with coins before moving in will bring prosperity.
No part of the house should cover or hang over the stump of a newly cut tree. Let it circulate by putting it in a bank or buying something with it. Please log in to use this service. Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Maliazurina Saad, Tae-Sun Choi Background The histological classification or subtyping of non-small cell lung cancer is essential for systematic therapy decisions.
Differentiating between the two main subtypes of pulmonary adenocarcinoma and squamous cell carcinoma highlights the considerable differences that exist in the prognosis of patient outcomes. Method A computational method that fuses two frameworks of computerized subtyping and prognosis was proposed, and it was validated against publicly available dataset in The Cancer Imaging Archive that consisted of 82 curated patients with CT scans.
The accuracy of the proposed method was compared with the gold standard of pathological analysis, as defined by the International Classification of Disease for Oncology ICD-O. A series of survival outcome test cases were evaluated using the Kaplan—Meier estimator and log-rank test p-value between the computational method and ICD-O. Results The computational method demonstrated high accuracy in subtyping The degree of reproducibility between prognosis taken on computational and pathological subtyping was substantial with an averaged concordance correlation coefficient CCC of 0.
Conclusion The findings in this study support the idea that quantitative analysis is capable of representing tissue characteristics, as offered by a qualitative analysis. Slimani, Mahdjoub Hamdi, M'hamed Bentourkia Monte Carlo MC simulation is widely recognized as an important technique to study the physics of particle interactions in nuclear medicine and radiation therapy.
There are different codes dedicated to dosimetry applications and widely used today in research or in clinical application, such as MCNP, EGSnrc and Geant4.
However, such codes made the physics easier but the programming remains a tedious task even for physicists familiar with computer programming. The calculation of the absorbed dose is performed based on 3D CT human or animal images in DICOM format, from images of phantoms or from solid volumes which can be made from any pure or composite material to be specified by its molecular formula.
G4DARI offers menus to the user and tabs to be filled with values or chemical formulas. The interface is described and as application, we show results obtained in a lung tumor in a digital mouse irradiated with seven energy beams, and in a patient with glioblastoma irradiated with five photon beams. In conclusion, G4DARI can be easily used by any researcher without the need to be familiar with computer programming, and it will be freely available as an application package.
However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately.
In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis ICCA feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other.
In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods.
Computerized Medical Imaging and Graphics, Volume Existing image segmentation solutions often lack accuracy when segmenting internal trabecular and cancellous bone tissues from adjacent soft tissues having similar appearance and often merge regions associated with distinct fragments.
These issues create problems in downstream visualization and pre-operative planning applications and impede the development of advanced image-based analysis methods such as virtual fracture reconstruction.
The proposed segmentation algorithm uses a probability-based variation of the watershed transform, referred to as the Probabilistic Watershed Transform PWT. The PWT uses a set of probability distributions, one for each bone fragment, that model the likelihood that a given pixel is a measurement from one of the bone fragments.
The likelihood distributions proposed improve upon known shortcomings in competing segmentation methods for bone fragments within CT images. A quantitative evaluation of the bone segmentation results is provided that compare our segmentation results with several leading competing methods as well as human-generated segmentations. Respiratory motion during the rotational acquisition challenges state-of-the-art reconstruction algorithms as intra-scan motion leads to inconsistencies causing substantial blurring and streaking artifacts in uncompensated reconstructions, suggesting the need for motion correction.
We propose an automated method for respiratory motion estimation and compensation based on registration of an initial 3D arterial model to vesselness enhanced 2D projection images.
This approach naturally allows for the estimation of 3D rigid translations as well as non-rigid deformations. Applied to a pre-clinical and a clinical acquisition, the proposed methods resulted in notable reductions in reprojection error and increased vessel sharpness that are reflected in less streaking and blurring artifact compared to the uncompensated case, implying superior vessel contrast. As the proposed methods are generic, future work will investigate their applicability to related rotational angiography imaging protocols, such as coronary angiography.
In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures ranging from the large organs to thin vessels can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN.
We utilize training and validation sets consisting of clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes CT scans, targeting three anatomical organs liver, spleen, and pancreas.
In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from We compare with a 2D FCN method on a separate dataset of CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results.
Muhammad Febrian Rachmadi, Maria del C. This is a rather difficult segmentation problem because of the small area i. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Analysis of covariance corroborated known associations between WMH progression, as assessed by all methods evaluated, and demographic and clinical data.
Deep learning algorithms outperform conventional machine learning algorithms by excluding MRI artefacts and pathologies that appear similar to WMH. Goncalves, Peng Cao, Dazhe Zhao, Arindam Banerjee Alzheimer's disease AD is a severe neurodegenerative disorder characterized by loss of memory and reduction in cognitive functions due to progressive degeneration of neurons and their connections, eventually leading to death.
In this paper, we consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment MCI , or Alzheimer's disease AD in a multi-task learning framework using features extracted from brain images obtained from ADNI Alzheimer's Disease Neuroimaging Initiative. To solve the problem, we present a multi-task sparse group lasso MT-SGL framework, which estimates sparse features coupled across tasks, and can work with loss functions associated with any Generalized Linear Models.
Through comparisons with a variety of baseline models using multiple evaluation metrics, we illustrate the promising predictive performance of MT-SGL on ADNI along with its ability to identify brain regions more likely to help the characterization Alzheimer's disease progression. Fang Chen, Ruibin Ma, Jia Liu, Mingyu Zhu, Hongen Liao Lumen and media—adventitia MA borders in intravascular ultrasound IVUS images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions.
However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model TEDM is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model.
An ensemble support vector machine classifier is used to classify IVUS pixels presented by texture features into different tissue types. The image regionalization maps of different tissue types are further used for texture enhancement modules in the TEDM. Evaluation results demonstrate that our method can accurately detect lumen and MA surfaces with small surface distance errors of 0.
The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography OCT data, while hardly any attempts have been made working on fundus images.
Fundus image analysis is a suitable tool in daily practice for an early detection of retinal diseases and the monitoring of their progression. Moreover, the fundus camera represents a low-cost and easy-access diagnostic system, which can be employed in resource-limited regions and countries.
The fundus images of a patient suffering from retinitis pigmentosa are characterized by an attenuation of the vessels, a waxy disc pallor and the presence of pigment deposits.
Considering that several methods have been proposed for the analysis of retinal vessels and the optic disk, this work focuses on the automatic segmentation of the pigment deposits in the fundus images.
The image distortions are attenuated by applying a local pre-processing. Next, a watershed transformation is carried out to produce homogeneous regions. Working on regions rather than on pixels makes the method very robust to the high variability of pigment deposits in terms of color and shape, so allowing the detection even of small pigment deposits. The regions undergo a feature extraction procedure, so that a region classification process is performed by means of an outlier detection analysis and a rule set.
The experiments have been performed on a dataset of images of patients suffering from retinitis pigmentosa. Although the images present a high variability in terms of color and illumination, the method provides a good performance in terms of sensitivity, specificity, accuracy and the F -measure, whose values are Lipoma arborescens LA , an infrequent intra-articular lesion, originates from mature adipose cells under subsynovial tissue.
The synovial membrane is pale yellow with large villous projections. It is caused by various underlying factors. We found many patients with LA and processed them appropriately. The research was implemented to investigate therapeutic effect of semi-automated arthroscopic diagnosis and treatment for knee joint.
Patients were chosen by biomechanical analysis and scanning mode. Arthroscopic limited synovectomy was carried out on these patients. Results The wound of all patients healed up. The content of follow-up includes: No swollen nor effusion of the infected knee was found in all patients during the follow-up.
The postoperative symptom was markedly alleviated in fourteen patients and partially alleviated in one. All patients were satisfied with the therapeutic effect. Conclusion We performed biomechanical analysis based on knee slight flexion and extension. Arthroscopy is an endoscope for the diagnosis and treatment of joint diseases.
Semi-automated arthroscopic debridement is good for early and mid-term osteoarthritis with Lipoma arborescens. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images.
Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features.
Secondly, the supervised phase is where various multi-class machine learning ML classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: Root mean square error RMSE and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent.
Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features.
Further, random forest RF outperforms other classifiers with F1 score 0. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment.
After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles. The true boundary pixels are classified using random forest trained by newly proposed cumulative zero count local binary pattern CZC-LBP and directional differential energy DDE along with Shannon, Tsallis entropy and intensity features.
Nizam Ahmed, Mrinal Mandal Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders.
Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. The proposed technique first extracts the brain area based on atlas registration and active contour model, and then performs template matching to obtain candidate cavernoma regions.
Texture, the histogram of oriented gradients and local binary pattern features of each candidate region are calculated, and principal component analysis is applied to reduce the feature dimensionality.
Support vector machines SVMs are finally used to classify each region into cavernoma or non-cavernoma so that most of the false positives obtained by template matching are eliminated. The performance of the proposed CAD system is evaluated and experimental results show that it provides superior performance in cavernoma detection compared to existing techniques.
Hongming Xu, Cheng Lu, Richard Berendt, Naresh Jha, Mrinal Mandal This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed.
In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed. Finally, the skin melanocytic image is classified into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine mSVM with extracted epidermis and dermis features. Available online 3 May Source: Computerized Medical Imaging and Graphics Author s: Reliable automatic extraction of WMH lesions is challenging due to their heterogeneous spatial occurrence, their small size and their diffuse nature.
In this paper, we present an automatic method to segment these lesions based on an ensemble of overcomplete patch-based neural networks. The proposed method successfully provides accurate and regular segmentations due to its overcomplete nature while minimizing the segmentation error by using a boosted ensemble of neural networks. The proposed method compared favourably to state of the art techniques using two different neurodegenerative datasets.
Available online 1 May Source: Methods This study imaging dataset consisted of three-dimensional 3D surface meshes of mandibular condyles constructed from cone beam computed tomography CBCT scans. The training dataset consisted of condyles, from control subjects and from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected.
The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology ShapeVariationAnalyzer, SVA , and a flexible web-based system for data storage, computation and integration DSCI of high dimensional imaging, clinical, and biological data.
The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. Conclusions The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis.
Available online 27 April Source: Methods Initially, 20 healthy volunteers, 21 cases with cryptogenic stroke, and 18 cases with migraine aura were randomly selected, and all of them received c-TTE and transesophageal echocardiography TEE examinations. First of all, 0.
In terms of c-TTE analyses, RLS could be diagnosed when micro bubbles were visualized in transition from the right atrium to the left atrium. Results A total of 20 healthy adult volunteers were identified into this research. In addition, the grading of PFO-RLS in patients suffering from cryptogenic stroke and migraine with aura was mostly grade 2 to grade 3. Conclusion P-RLS with lower semiquantitative grade is common in healthy individuals, patients with cryptogenic stroke and migraine aura.
And, P-RLS can be considered as a significant influencing factor in the pathogenesis of migraine with aura. Available online 26 April Source: Zhexin Jiang, Hao Zhang, Yi Wang, Seok-Bum Ko Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems.
In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging.
Meanwhile, additional unsupervised image post-processing techniques are applied to this proposed method so as to refine the final result. This successful result has not only contributed to the area of automated retinal blood vessel segmentation but also supports the effectiveness of transfer learning when applying deep learning technique to medical imaging.
Available online 25 April Source: Ying Li, Yu Ye, Yang Mei, Haiying Ruan, Yuan Yu Objective To evaluate the application value of semi-automated ultrasound on the guidance of nasogastrojejunal tube replacement for patients with acute severe pancreatitis ASP , as well as the value of the nutritional support for standardized treatment in clinical practice.
Methods The retrospective research was performed in our hospital, and 34 patients suffering from ASP were enrolled into this study. All these identified participants ever received CT scans in order to make definitive diagnoses. Following, these patients received semi-automated ultrasound examinations within 1 days after their onset, in order to provide enteral nutrititon treatment via nasogastrojejunal tube, or freehand nasogastrojejunal tube replacement.
In terms of statistical analysis, the application value of semi-automated ultrasound guidance on nasogastrojejunal tube replacement was evaluated, and was compared with tube replacement of no guidance. After cathetering, the additional enteral nutrition was provided, and its therapeutic effect on SAP was analyzed in further. Results A total of 34 patients with pancreatitis were identified in this research, 29 cases with necrosis of pancreas parenchyma.
After further examinations, 32 cases were SAP, 2 cases were mild acute pancreatitis. When the firm diagnosis was made, additional enteral nutrition EN was given, all the patient conditions appeared good, and they all were satisfied with this kind of nutritional support.
Additionally, the comparison between ultrasound-guided and freehand nasogastrojejunal tube replacement was made. Conclusions It can be indicated that semi-automated ultrasound guidance is a reliable method for nasogastrojejunal tube replacement, and should be substituted for no guidance of cathetering.
In terms of therapeutic effect of EN, additional nutritional support contributed to significantly improve the prognosis of SAP patients, and should be widely recommended in clinical practice. Surely, this conclusion should be evaluated in further, by means of randomized controlled trials and economic evaluation. Available online 21 April Source: Methods Ten healthy volunteers were enrolled, which included 4 males and 6 females, aged years old with median age of The statistically different brain regions were obtained by false discovery rate corrected FDR-Corrected.
Results Compared with control group, the anterior cingulated gyrus, left temporal gyrus, right inferior parietal lobule, right frontal gyrus were enhanced ReHo after acupuncture at GB The left thalamus, right insular cortex, left inferior frontal gyrus, right anterior cingulate were decreased ReHo after acupuncture at GB Conclusion It is demonstrated that the signal synchronization change ReHo in different brain regions including cognitive, motor, default network, limbic system and other parts of encephalic region after acupuncture at GB34, suggesting that the central mechanism of acupuncture at GB34 is the result of all levels of the combined effects of brain networks.
Available online 13 April Source: Fons van der Sommen, Sander R. Volumetric Laser Endomicroscopy VLE is a promising new tool for finding this type of cancer early, capturing a full circumferential scan of Barrett's Esophagus BE , up to 3-mm depth.
However, the interpretation of these VLE scans can be complicated, due to the large amount of cross-sectional images and the subtle grayscale variations. Therefore, algorithms for automated analysis of VLE data can offer a valuable contribution to its overall interpretation.
We employ a histopathologically validated set of ex-vivo VLE images for evaluating and comparing a considerable set of widely-used image features and machine learning algorithms. In addition, we show that incorporating clinical knowledge in feature design, leads to a superior classification performance and additional benefits, such as low complexity and fast computation time.
Furthermore, we identify an optimal tissue depth for classification of 0. Available online 3 April Source: Imad Zyout, Roberto Togneri Achieving a high performance for the detection and characterization of architectural distortion in screening mammograms is important for an efficient breast cancer early detection. Viewing a mammogram image as a rough surface that can be described using the fractal theory is a well-recognized approach. This paper presents a new fractal-based computer-aided detection CAD algorithm for characterizing various breast tissues in screening mammograms with a particular focus on distinguishing between architectural distortion and normal breast parenchyma.
The proposed approach is based on two underlying assumptions: The obtained results validate the underlying hypothesis and demonstrate that effectiveness of capturing the variation of the fractal dimension measurements within an appropriate multiscale representation of the digital mammogram. Results also reveal that this tool has the potential of prescreening other key and common mammographic signs of early breast cancer.
Ewa Pietka, Arkadiusz Gertych. Sylwester Fabian, Dominik Spinczyk Precise positioning of the target point during minimally invasive procedures is a major challenge associated with the use of image-based navigation systems. However, this investigation demonstrated the utility of using thin plate splines TPS and marker observation to monitor FRE during respiration to estimate target position based on the deformation field for minimally invasive procedures in deformable regions.
The proposed methodology was verified via experiments involving 21 patients diagnosed with liver tumors. This method has been developed for real-time use while performing operations. This study proposed respiratory motion-compensation rMoCo employing non-negative matrix factorization combined with fast block matching algorithm to effectively remove these disturbances on abdominal PPI, which was validated through in-vivo perfusion experiments.
The mean calculation efficiency of rMoCo was improved by The horizontal and vertical displacements induced by respiratory kinetics were estimated to correct the extraction of time-intensity curves and the peak SNR remained at Compared with the results without rMoCo, the continuity and visualization of abdominal arterioles were clearly enhanced, and their perfusion details were accurately characterized by PPIs with non-negative rMoCo.
The proposed method benefits clinicians in providing accurate diagnoses and in developing appropriate therapeutic strategies for abdominal diseases. Automatization of this procedure may help to develop bloodbot rigs and improve use of image guided surgery. Method It is not necessary to have a full 3D model in order to determine their location by calculating the spatial coordinates of veins axes in the adopted coordinate system.
A much better solution is pre-segmentation, which provides veins axes, and further search for stereo correspondence in the segmented images. The computational complexity of this approach is much smaller, which ensures its quick operation. A disparity map necessary for the calculation of spatial coordinates is created according to the principle that the most likely correct distance between homologous elements is the minimum distance value.