Improved Guided Image Fusion for Magnetic Resonance and Computed Tomography Imaging

Jameel, Amina; Ghafoor, Abdul; Riaz, Muhammad Mohsin

doi:10.1155/2014/695752

Cited by 17 publications

(16 citation statements)

References 37 publications

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“…The scientific community has established three levels of fusion rules: image level (pixel or iconic level), feature level (regional or object level) and decision level (information level) [7,20,21]. Others classify image fusion into four levels which includes data level (signal level).…”

Section: Fusion Levelsmentioning

confidence: 99%

“…ii. Image level fusion, also called image fusion, is performed on a pixel-by-pixel basis with less complexity and preservation of most of the original content [7,21]. iii.…”

Section: Fusion Levelsmentioning

confidence: 99%

“…iii. Feature level fusion uses a group of image pixel to form contiguous regions and requires extraction of different features from source data [7,21]. Features can be pixel intensities, edges or texture features [22,24].…”

Section: Fusion Levelsmentioning

confidence: 99%

“…iv. Decision level fusion is a high level fusion [22] that combines results from multiple algorithms to yield final fused decision [7,21].…”

Section: Fusion Levelsmentioning

confidence: 99%

“…CT uses an ionizing X-ray radiation to generate very thin slice images from different angles of anatomical structures of the body [15,21]. It produces still picture X-ray scans from different angles.…”

Section: Imaging Modalitiesmentioning

confidence: 99%

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Multimodal Medical Image Fusion in Cardiovascular Applications

Pohl

Nazirun

Hamzah

et al. 2015

Lecture Notes in Bioengineering

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Recent publications in the field of medical image fusion point out the value of multi-modality in diagnosis, pre-surgical planning and surgical intervention. The integration of multiple data sources including medical images from different devices or sensors strongly increases reliability and information content. Successfully fused multi-modal data should not contain any artefacts, not remove any relevant information from the original data and minimize redundancy. Image and data fusion aims at providing supplementary clinical information that is not apparent in the individual images alone. Image and data fusion finds many different applications in the fields of remote sensing, military, biometrics, machine vision and medical imaging. The scientific community has established three levels of fusion rules, namely pixel, feature and decision level. Depending on the application, processing technique or available data each level has its importance and proven significance in medical data processing. Each level provides a set of rules that can be applied. The selection of the fusion operator has a strong impact on the quality of the result. It becomes apparent that the selection of level and technique must vary according to the information that needs to be extracted for a certain application. Each technique has its advantages and disadvantages which have to be carefully evaluated. Based on the availability of multimodal devices, such as ultrasound (US), magnetic resonance imaging (MRI) and computed tomography (CT), different images and data of the same object are obtained. The multiple images, the variety of fusion levels and rules lead to an uncountable number of possible combinations. This makes it very difficult for the user to select the most beneficial solution without losing valuable time and resources.

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Section: Fusion Levelsmentioning

confidence: 99%

“…ii. Image level fusion, also called image fusion, is performed on a pixel-by-pixel basis with less complexity and preservation of most of the original content [7,21]. iii.…”

Section: Fusion Levelsmentioning

confidence: 99%

Section: Fusion Levelsmentioning

confidence: 99%

“…iv. Decision level fusion is a high level fusion [22] that combines results from multiple algorithms to yield final fused decision [7,21].…”

Section: Fusion Levelsmentioning

confidence: 99%

Section: Imaging Modalitiesmentioning

confidence: 99%

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Multimodal Medical Image Fusion in Cardiovascular Applications

Pohl

Nazirun

Hamzah

et al. 2015

Lecture Notes in Bioengineering

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Multifunctional Fiber‐Enabled Intelligent Health Agents

Chen

Wang

et al. 2022

Advanced Materials

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The application of wearable devices is promoting the development toward digitization and intelligence in the field of health. However, the current smart devices centered on human health have disadvantages such as weak perception, high interference degree, and unfriendly interaction. Here, an intelligent health agent based on multifunctional fibers, with the characteristics of autonomy, activeness, intelligence, and perceptibility enabling health services, is proposed. According to the requirements for healthcare in the medical field and daily life, four major aspects driven by intelligent agents, including health monitoring, therapy, protection, and minimally invasive surgery, are summarized from the perspectives of materials science, medicine, and computer science. The function of intelligent health agents is realized through multifunctional fibers as sensing units and artificial intelligence technology as a cognitive engine. The structure, characteristics, and performance of fibers and analysis systems and algorithms are reviewed, while discussing future challenges and opportunities in healthcare and medicine. Finally, based on the above four aspects, future scenarios related to health protection of a person's life are presented. Intelligent health agents will have the potential to accelerate the realization of precision medicine and active health.

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