scholarly journals Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 642-647
Keyword(s):  
Computation Time ◽  
Medical Practitioner ◽  
Automated Analysis ◽  
Disease Diagnosis ◽  
Tumor Location ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Computer Assisted ◽  
Accurate Analysis ◽  
Tumor Region

Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

ECG Analysis: A Brief Review

2019 ◽  
Vol 12 ◽  
Author(s):  
Saumendra Kumar Mohapatra ◽  
Mihir Narayan Mohanty
Keyword(s):  
Technology Development ◽  
Accurate Result ◽  
Early Stage ◽  
Heart Diseases ◽  
Short Review ◽  
Disease Diagnosis ◽  
Accurate Analysis ◽  
Ecg Analysis ◽  
Proper Analysis ◽  
Living Being

Background: In recent years cardiac problems found proportional to technology development. Cardiac signal (Electrocardiogram) relates to the electrical activity of the heart of a living being and it is an important tool for diagnosis of heart diseases. Method: Accurate analysis of ECG signal can provide support for detection, classification, and diagnosis. Physicians can detect the disease and start the diagnosis at an early stage. Apart from cardiac disease diagnosis ECG can be used for emotion recognition, heart rate detection, and biometric identification. Objective: The objective of this paper is to provide a short review of earlier techniques used for ECG analysis. It can provide support to the researchers in a new direction. The review is based on preprocessing, feature extraction, classification, and different measuring parameters for accuracy proof. Also, different data sources for getting the cardiac signal is presented and various application area of the ECG analysis is presented. It explains the work from 2008 to 2018. Conclusion: Proper analysis of the cardiac signal is essential for better diagnosis. In automated ECG analysis, it is essential to get an accurate result. Numerous techniques were addressed by the researchers for the analysis of ECG. It is important to know different steps related to ECG analysis. A review is done based on different stages of ECG analysis and its applications in society.

Determination of the Minimum Distance Between Moving Objects Including Velocity Information

1993 ◽  
Author(s):  
Meyer Nahon
Keyword(s):  
Minimum Distance ◽  
Moving Objects ◽  
Rapid Determination ◽  
Computation Time ◽  
Optimization Techniques ◽  
Real Time Control ◽  
Time Step ◽  
Time Control ◽  
Velocity Information

Abstract The rapid determination of the minimum distance between objects is of importance in collision avoidance for a robot maneuvering among obstacles. Currently, the fastest algorithms for the solution of this problem are based on the use of optimization techniques to minimize a distance function. Furthermore, to date this problem has been approached purely through the position kinematics of the two objects. However, although the minimum distance between two objects can be found quickly on state-of-the-art hardware, the modelling of realistic scenes entails the determination of the minimum distances between large numbers of pairs of objects, and the computation time to calculate the overall minimum distance between any two objects is significant, and introduces a delay which has serious repercussions on the real-time control of the robot. This paper presents a technique to modify the original optimization problem in order to include velocity information. In effect, the minimum distance calculation is performed at a future time step by projecting the effect of present velocity. This method has proven to give good results on a 6-dof robot maneuvering among obstacles, and has allowed a complete compensation of the lags incurred due to computational delays.

Computer assisted evaluation of plate osteosynthesis of diaphyseal femur fracture considering interfragmentary movement: a finite element study

2017 ◽  
Vol 62 (3) ◽  
Author(s):  
Claudia Wittkowske ◽  
Stefan Raith ◽  
Maximilian Eder ◽  
Alexander Volf ◽  
Jan S. Kirschke ◽  
...  
Keyword(s):  
Finite Element ◽  
Plate Osteosynthesis ◽  
Computation Time ◽  
Fe Analysis ◽  
Patient Specific ◽  
Osteoporotic Bone ◽  
Computer Assisted ◽  
Efficient Computation ◽  
User Input ◽  
Patient Specific Modeling

AbstractA semi-automated workflow for evaluation of diaphyseal fracture treatment of the femur has been developed and implemented. The aim was to investigate the influence of locking compression plating with diverse fracture-specific screw configurations on interfragmentary movements (IFMs) with the use of finite element (FE) analysis. Computed tomography (CT) data of a 22-year-old non-osteoporotic female were used for patient specific modeling of the inhomogeneous material properties of bone. Hounsfield units (HU) were exported and assigned to elements of a FE mesh and converted to mechanical properties such as the Young’s modulus followed by a linear FE analysis performed in a semi-automated fashion. IFM on the near and far cortex was evaluated. A positive correlation between bridging length and IFM was observed. Optimal healing conditions with IFMs between 0.5 mm and 1 mm were found in a constellation with a medium bridging length of 80 mm with three unoccupied screw holes around the fracture gap. Usage of monocortical screws instead of bicortical ones had negligible influence on the evaluated parameters when modeling non-osteoporotic bone. Minimal user input, automation of the procedure and an efficient computation time ensured quick delivery of results which will be essential in a future clinical application.

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

scholarly journals Fast and Accurate Finite Transducer Analysis Method for Wireless Passive Impedance-Loaded SAW Sensors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3988
Author(s):  
Wei Luo ◽  
Yang Yuan ◽  
Yi Wang ◽  
Qiuyun Fu ◽  
Hui Xia ◽  
...  
Keyword(s):  
Computation Time ◽  
Processing Unit ◽  
Bulk Wave ◽  
Analysis Method ◽  
Accurate Analysis ◽  
Central Processing ◽  
Finite Transducer ◽  
Long Time ◽  
High Degree ◽  
Element Method

An accurate and fast simulation tool plays an important role in the design of wireless passive impedance-loaded surface acoustic wave (SAW) sensors which have received much attention recently. This paper presents a finite transducer analysis method for wireless passive impedance-loaded SAW sensors. The finite transducer analysis method uses a numerically combined finite element method-boundary element method (FEM/BEM) model to analyze non-periodic transducers. In non-periodic transducers, FEM/BEM was the most accurate analysis method until now, however this method consumes central processing unit (CPU) time. This paper presents a faster algorithm to calculate the bulk wave part of the equation coefficient which usually requires a long time. A complete non-periodic FEM/BEM model of the impedance sensors was constructed. Modifications were made to the final equations in the FEM/BEM model to adjust for the impedance variation of the sensors. Compared with the conventional method, the proposed method reduces the computation time efficiently while maintaining the same high degree of accuracy. Simulations and their comparisons with experimental results for test devices are shown to prove the effectiveness of the analysis method.

A survey on computer-assisted Parkinson's Disease diagnosis

2019 ◽  
Vol 95 ◽  
pp. 48-63 ◽  
Author(s):  
Clayton R. Pereira ◽  
Danilo R. Pereira ◽  
Silke A.T. Weber ◽  
Christian Hook ◽  
Victor Hugo C. de Albuquerque ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Diagnosis ◽  
Computer Assisted

scholarly journals Computer-assisted framework for machine-learning–based delineation of GTV regions on datasets of planning CT and PET/CT images

2017 ◽  
Vol 58 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Koujiro Ikushima ◽  
Hidetaka Arimura ◽  
Ze Jin ◽  
Hidetake Yabu-uchi ◽  
Jumpei Kuwazuru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Glass Opacity ◽  
Global Optimum ◽  
Image Features ◽  
Support Vector ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Radiation Oncologists ◽  
Tumor Region ◽  
Leave One Out

Abstract We have proposed a computer-assisted framework for machine-learning–based delineation of gross tumor volumes (GTVs) following an optimum contour selection (OCS) method. The key idea of the proposed framework was to feed image features around GTV contours (determined based on the knowledge of radiation oncologists) into a machine-learning classifier during the training step, after which the classifier produces the ‘degree of GTV’ for each voxel in the testing step. Initial GTV regions were extracted using a support vector machine (SVM) that learned the image features inside and outside each tumor region (determined by radiation oncologists). The leave-one-out-by-patient test was employed for training and testing the steps of the proposed framework. The final GTV regions were determined using the OCS method that can be used to select a global optimum object contour based on multiple active delineations with a LSM around the GTV. The efficacy of the proposed framework was evaluated in 14 lung cancer cases [solid: 6, ground-glass opacity (GGO): 4, mixed GGO: 4] using the 3D Dice similarity coefficient (DSC), which denotes the degree of region similarity between the GTVs contoured by radiation oncologists and those determined using the proposed framework. The proposed framework achieved an average DSC of 0.777 for 14 cases, whereas the OCS-based framework produced an average DSC of 0.507. The average DSCs for GGO and mixed GGO were 0.763 and 0.701, respectively, obtained by the proposed framework. The proposed framework can be employed as a tool to assist radiation oncologists in delineating various GTV regions.

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