Segmentation of fused MR and CT images using DL-CNN with PGK and NLEM filtered AACGK-FCM

Reddy, Mummadi Gowthami; Reddy, Palagiri Veera Narayana; Reddy, P. Ramana

doi:10.1016/j.bspc.2021.102618

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Also among the most important use of FCM technology, is to divide the tumor from the merged images between MR and CT images. 13 The authors in Reference 14, proposed a new level set method that is called Fuzzy Kernel Level Set (FKLS) for 3D brain tumor segmentation in MR images. Experimental results showed that the obtained segmentation accuracy is greater than 95%.…”

Section: Related Workmentioning

confidence: 99%

“…The results obtained in the work, 12 embodied the objectives of the ruler by the researchers, which was aimed at modifying the objective function in the FCM technique, using a double estimation between the original and noise‐enriched images. Also among the most important use of FCM technology, is to divide the tumor from the merged images between MR and CT images 13 …”

Section: Related Workmentioning

confidence: 99%

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A 3D visualization‐based augmented reality application for brain tumor segmentation

Guerroudji,

Amara,

Lichouri

et al. 2023

Computer Animation & Virtual

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SummaryEvery year on June 8th, the globe observes World Brain Tumor Day to raise awareness and educate people about brain cancer, encompassing both noncancerous (benign) and cancerous (malignant) growths. Research in the field of brain cancer plays a vital role in supporting medical professionals. In this context, augmented reality (AR) technology has emerged as a valuable tool, enabling surgeons to visualize underlying structures and offering a cost‐effective and time‐efficient alternative. Our study focuses on the efficient segmentation of brain tumor classes using Magnetic Resonance Imaging (MRI) and incorporates a three‐stage approach: preprocessing, segmentation, and 3D reconstruction & AR display. In the preprocessing stage, a Gaussian filter is applied to mitigate intensity heterogeneity. Segmentation and detection are achieved using active geometric contour models, complemented by morphological operations. To establish 3D brain tumor reconstruction, a genuine scene is virtually integrated using 3D Slicer software. The proposed methodology was validated using a genuine patient dataset comprising 496 MRI scans obtained from the local Bab El Oued university hospital center. The results demonstrate the effectiveness of our approach in achieving accurate 3D brain tumor reconstruction, efficient tumor extraction, and augmented reality visualization. The obtained segmentation results showcased an impressive accuracy of 98.61%, outperforming existing state‐of‐the‐art methods and affirming the efficacy of our proposed strategy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A 3D visualization‐based augmented reality application for brain tumor segmentation

Guerroudji,

Amara,

Lichouri

et al. 2023

Computer Animation & Virtual

View full text Add to dashboard Cite

show abstract

“…The approach produced mutual information of 2.8059 and a structural similarity index of 0.8551. To extract highlevel features and fuse MRI and CT images, Reddy et al [48] developed a fusion method utilizing a convolutional neural network (CNN) with a pyramidal generating kernel. With the addition of non-local Euclidean median filtering adaptive angular covariance, FCM clustering based on Gaussian kernel, and segmenting brain tumors from fused pictures, the tumor segmentation method for cancer analysis and detection has become extremely accurate and effective.…”

Section: Multimodal Fusion Of Brain Diseasesmentioning

confidence: 99%

Improvement and Application of Fusion Scheme in Automatic Medical Image Analysis

Liang¹,

Wei²,

Li³

et al. 2023

AJST

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The research in this paper provides generalization and new ideas for research topics in computer-assisted medicine. The main improvement efforts in deep learning-based multimodal fusion schemes, which provide alternative directions and robust feature fitting performance for fusion schemes, are building complex structures, migrating knowledge or experience, processing and enhancing data, and targeting features for semantic correction based on contextual features. At the application level, the brain, liver, and lungs are the main targets of scientific research, so this paper surveys related work and analyzes the reasons for performance gains. Taken together, deep learning-based image fusion schemes can assist physicians in understanding information about lesion sites, lesion types, and sizes, providing an important basis for developing personalized treatment plans, which is important for improving diagnosis and specifying precise treatment plans. Therefore, the investigation of medical image fusion schemes is promising and beneficial.

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A systematic survey of fuzzy deep learning for uncertain medical data

Zheng,

Xu,

et al. 2024

Artif Intell Rev

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Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

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Segmentation of fused MR and CT images using DL-CNN with PGK and NLEM filtered AACGK-FCM

Cited by 8 publications

References 28 publications

A 3D visualization‐based augmented reality application for brain tumor segmentation

A 3D visualization‐based augmented reality application for brain tumor segmentation

Improvement and Application of Fusion Scheme in Automatic Medical Image Analysis

A systematic survey of fuzzy deep learning for uncertain medical data

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