다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain. In this study, we have concentrated on noise removal technique, extraction of gray-level co-occurrence matrix (GLCM) features, DWT-based brain tumor region growing segmentation to reduce the complexity and improve the performance. This was followed by morphological filtering which removes the noise that can be formed after segmentation. The probabilistic neural network classifier was used to train and test the performance accuracy in the detection of tumor location in brain MRI images. The experimental results achieved nearly 100% accuracy in identifying normal and abnormal tissues from brain MR images demonstrating the effectiveness of the proposed technique.

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Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

A review on brain tumor segmentation of MRI images.

Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019

Brain tumor detection using statistical and machine learning method.

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

/pdf/image-analysis-for-mri-based-brain-tumor-detection-and-583xpc0g82.pdf

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

/pdf/comparative-approach-of-mri-based-brain-tumor-segmentation-2wb2ly4t8y.pdf

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm

Users often need to control and monitor activities inside the home remotely, for elderly parents, children, or pets. This paper presents a multifunctional, low-cost, and flexible system for smart home monitoring and control. The system is based on node-MCU ESP32 with Internet connectivity that allows remote device control. The system transmits sensor data to the Firebase database and can receive commands from the server, allowing automatic control. The android-based mobile app is designed for communication with the Firebase database and updating its values to monitor and control the various home appliances. For the demonstration of the feasibility of the system, various sensors like temperature, humidity, light, LPG (MQ-6), and motion sensors were integrated into a prototype of the home automation system. For the authentication and validation of the proposed system, the performance of the system is studied rigorously and is also evaluated.

Internet of Things based Integrated Smart Home Automation System

Brain tumor detection and extraction within the time frame to offer better healthcare is vital and very important, but a time-consuming task performed by clinical supervisors or radiologists Its accuracy for the brain tumor detection from modern imaging modalities also depends on their experience only So the use of computer-aided methodology is very important to overcome these limitations Our proposed technique uses feature extraction and optimization of the extracted features based on their relevance to detect brain tumor from the magnetic resonance images By optimizing extracted features, only relevant features are retained for further analysis and so reduce the mathematical complexity of classification of the brain tumor and so detect the abnormalities at a fast rate with higher accuracy as compared to manual detection The experimental results perform on the different brain magnetic resonance images obtained an average of 073 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists

Feature extraction and selection with optimization technique for brain tumor detection from MR images

An Integration of image processing and soft computing techniques for the image analysis plays a significant contribution in the field of image processing. One of the important class of image analysis techniques is image segmentation. There are applications in various fields some of them are sorting product in the industry, surveillance system in the security zones, biomedical processing, medical imaging, environmental predictions etc. In this paper the study of fuzzy C-means clustering method, watershed based image segmentation and Simulink design based image segmentation with some enhancement design algorithm for the improvement of the overall performance of image segmentation is used. The main objective of this paper is to bring the conviction of utility of watershed, Simulink and FCM based segmentation for the applications in medical imaging, and other images where lots of spatial & inherent information is available, by considering their performance analysis metrics. This performance analysis is used to justify and decide which method is appropriate and more convincing for the effective analysis of more complex imaging system.

Nilesh Bhaskarrao Bahadure

Papers

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm

Internet of Things based Integrated Smart Home Automation System

Feature extraction and selection with optimization technique for brain tumor detection from MR images

Performance analysis of image segmentation using watershed algorithm, fuzzy C-means of clustering algorithm and Simulink design