Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images

Gutierrez-Caceres, Juan C.; Portugal-Zambrano, Christian E.; Beltrán, César

doi:10.1109/cbms.2014.110

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Electroencephalography (EEG) signal has been used purposely for communication between the human brain and a computer system, to diagnose epilepsy [76] and other brain disorders [77]. Currently, this signal capability is extended to establish communication between the human brain and a robotic system [78], and to send a control signal from a human to a robotic system [79].…”

Section: Sensors and Actuatorsmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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Section: Sensors and Actuatorsmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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“…As fuzzy c-mean clustering for the image segmentation than GLRLM(Grey Level Run Length Matrix) used to extract the information from the image of brain whereas SVM is for the dividing the MRI images into different categories(or to classify the brain images) to analyze by the neurosurgeon to find that patient having tumor or not. Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images [7] This paper title present the work done on the preparation of the tool to identify tumor in brain. The proposed system performs in following sequence and generates the effective results.…”

Section: Literature Surveymentioning

confidence: 99%

Brain Tumor Detection using Image Segmentation Techniques on MRI Images

Sharma*¹,

Marwaha²

2020

IJITEE

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A refine and absolute detection of the human disease can increase the level for the survival of well being. One of the major diseases can be determine as cancer or brain tumor. Different technique used for the tumor detection such as MRI (Medical Resonance Image), Ultrasound etc. It does not involve the X-ray so there are not side effects of MRI. Claustrophobic sensation can occur with MRI scanning because of the close machine with the suffocation and no movement while the scan can increase the time to complete the scanning process while movement can cause the errors or edge distortion. An MRI or magnetic resonance imaging is a radiology technique scan that uses radio waves and with the help of computer to produce images of different body parts. MRI scanner is a tube surrounded by a giant circular magnet. The patient is placed on a moveable bed that is inserted into the magnet. The magnet creates a strong magnetic field that aligns the protons of hydrogen atoms, which are then exposed to a beam of radio waves. A computer receives the information, which produces an image. MRI is the most accurate and safe method to detect the diseases by detecting the slight change in the body. For the best result various image segmentation techniques and the good edge detection algorithms are used for finding the edge to get the effective images for the examination purpose of medical images.

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“…In [27], a five-step approach was followed to build a computer-assisted medical diagnosis tool to detect MRI images. In the first step, a Gabor filter was used to extract the texture features.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Exploring Mediatoil Imagery: A Content-based Approach

Saroop¹,

Viktor

McCurdy

et al. 2017

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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The future of Alberta's bitumen sands, also known as "oil sands" or "tar sands," and their place in Canada's energy future has become a topic of much public debate. Within this debate, the print, television, and social media campaigns of those who both support and oppose developing the oil sands are particularly visible. As such, campaigns around the oil sands may be seen as influencing audience perceptions of the benefits and drawbacks of oil sands production. There is consequently a need to study the media materials of various tar sands stakeholders and explore how they differ. In this setting, it is essential to gather documents and identify content within images, which requires the use of an image retrieval technique such as a content-based image retrieval (CBIR) system. In a CBIR system, images are represented by low-level features (i.e. specific structures in the image such as points, edges, or objects), which are used to distinguish pictures from one another.The oil sands domain has to date not been mapped using CBIR systems. The research thus focuses on creating an image retrieval system, namely Mediatoil-IR, for exploring documents related to the oil sands. Our aim is to evaluate various low-level representations of the images within this context. To this end, our experimental framework employs LAB color histogram (LAB) and speeded up robust features (SURF) in order to typify the imagery. We further use machine learning techniques to improve the quality of retrieval (in terms of both accuracy and speed). To achieve this aim, the extracted features from each image are encoded in the form of vectors and used as a training set for learning classification models to organize pictures into different categories. Different algorithms were considered such as Linear SVM, Quadratic SVM, Weighted KNN, Decision Trees, Bagging, and Boosting on trees. It was shown that Quadratic SVM algorithm trained on SURF features is a good approach for building CBIR, and is used in building Mediatoil-IR.Finally, with the help of created CBIR, we were able to extract the similar documents and explore the different types of imagery used by different stakeholders. Our experimental evaluation shows that our Mediatoil-IR system is able to accurately explore the imagery used by different stakeholders.iii

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Computer Aided Medical Diagnosis Tool to Detect Normal/Abnormal Studies in Digital MR Brain Images

Cited by 6 publications

References 10 publications

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Brain Tumor Detection using Image Segmentation Techniques on MRI Images

Exploring Mediatoil Imagery: A Content-based Approach

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