A survey on multi - modality medical image fusion

Bhavana, V; Krishnappa, H K

doi:10.1109/wispnet.2016.7566352

Cited by 14 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CT scanned images of lungs have water, fat, bones, tissues etc.so the table 1 shows different attenuation values of different tissues in our body [6]. According to the reference paper [7] limitations of that are GGO and juxeta-pleural nodules are not addressed and it was limited to cross validation. By using EM algorithm there is a limitation of missing nodules which are less than 5mm but it's applicability in some regions is very affective [8].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Research on Different Classifiers for Early Detection of Lung Nodules

2019

ijrte

View full text Add to dashboard Cite

Lung cancer is one of the most common deadliest disease which has highest death rate as per the recent medical research. However, research indicates that early detection of lung cancer improves chances of survival. The disease is identified using nodules attached to lung walls and lung parenchyma. Nodules plays significant role in identifying cancer in lungs. The proposed approach to determine lung nodules has three stages preprocessing, feature extraction and classification. Segmentation is the preprocessing technique involves two phases namely lung parenchyma segmentation and lung nodules segmentation. Then, texture features and geometric features are extracted using feature extraction algorithms. Lastly, using classification techniques the nodules are classified as benign or malign. TCIA dataset was used for validation of the proposed approach. Form the dataset, CT images were used which have high density resolution and adequate information which helps to find every small detail easily. The proposed method helps in improving accuracy to find number of the lung nodules in lung region and also helps is differentiating benign and malign nodules using CNN architecture. Different classifiers such as SVM, MLP and CNN classifiers are used in comparison analysis. As the result, we conclude that the approach of feature extraction with CNN decreases the false positive rate significantly compared to the existing classification approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Nodules are 3D structural components which have both geometric and textural features. For geometric features binary mask is used and gray scale values are used for the textural features [7].…”

Section: Fig1: Block Diagram Of the Proposed Approachmentioning

confidence: 99%

Research on Different Classifiers for Early Detection of Lung Nodules

2019

ijrte

View full text Add to dashboard Cite

show abstract

“…Medical imaging technology refers to a noninvasive way to obtain contrast images of the internal tissue structures in the human body. Imaging techniques such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have been widely applied in clinical practice with crucial information for disease diagnosis and treatment 1 . Particularly, MRI has been adopted as the primary imaging modality in neuroanatomy 2 for its superiority in multimodal imaging and clear display of soft tissue structures in the human body.…”

Section: Introductionmentioning

confidence: 99%

“…Imaging techniques such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have been widely applied in clinical practice with crucial information for disease diagnosis and treatment. 1 Particularly, MRI has been adopted as the primary imaging modality in neuroanatomy 2 for its superiority in multimodal imaging and clear display of soft tissue structures in the human body. By using different pulse sequences and scanning parameters, multiple MRI modalities corresponding to the same anatomical structure can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based multi‐modal computing with feature disentanglement for MRI image synthesis

et al. 2021

View full text Add to dashboard Cite

Purpose Different Magnetic resonance imaging (MRI) modalities of the same anatomical structure are required to present different pathological information from the physical level for diagnostic needs. However, it is often difficult to obtain full‐sequence MRI images of patients owing to limitations such as time consumption and high cost. The purpose of this work is to develop an algorithm for target MRI sequences prediction with high accuracy, and provide more information for clinical diagnosis. Methods We propose a deep learning‐based multi‐modal computing model for MRI synthesis with feature disentanglement strategy. To take full advantage of the complementary information provided by different modalities, multi‐modal MRI sequences are utilized as input. Notably, the proposed approach decomposes each input modality into modality‐invariant space with shared information and modality‐specific space with specific information, so that features are extracted separately to effectively process the input data. Subsequently, both of them are fused through the adaptive instance normalization (AdaIN) layer in the decoder. In addition, to address the lack of specific information of the target modality in the test phase, a local adaptive fusion (LAF) module is adopted to generate a modality‐like pseudo‐target with specific information similar to the ground truth. Results To evaluate the synthesis performance, we verify our method on the BRATS2015 dataset of 164 subjects. The experimental results demonstrate our approach significantly outperforms the benchmark method and other state‐of‐the‐art medical image synthesis methods in both quantitative and qualitative measures. Compared with the pix2pixGANs method, the PSNR improves from 23.68 to 24.8. Moreover the ablation studies have also verified the effectiveness of important components of the proposed method. Conclusion The proposed method could be effective in prediction of target MRI sequences, and useful for clinical diagnosis and treatment.

show abstract

“…V and Krishnappa. H.K wrote a survey paper" A Survey on Multi -Modality Medical Image Fusion" in (2016) [23], they explained with details the modalities that had been used in different techniques for family of wavelet methods with fusing various modalities (MRI, CT, PET, SPECT) of brain images in medical field into a distinct image with more detailed anatomical information and high spectral information is highly desired in clinical diagnosis. Detailed survey concluded that all these techniques mentioned above have either a serious cons of color drawbacks, visual clarity or less informative in the gray matter area (GM) of the high-activity region of the fused image.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases

Yousif

Omar

Sheikh

2020

JBEMI

View full text Add to dashboard Cite

Brain diseases such as degenerative (alzheimer's disease), neoplastic disease (brain tumor like sarcoma, glioma) are considered an interesting topic areas in the medical image fusion diagnosis. Pixel-level image fusion techniques are designed to combine multiple/multi-scale input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Since they are difficult to be summarized ; survey paper are characterized by (1) medical image definition , brain diseases challenges , analysis a various techniques for multi-scale image fusion with its own modalities, fusion rule, fusion strategy and dis-advantage ,Whilst used a database of medical images for medical Harvard School (brain diseases) which contains various groups of co-registered multi-modal images including MRI/CT, MRI/PET and PET/SPECT and MRI (T1/T2) images.

show abstract

A survey on multi - modality medical image fusion

Cited by 14 publications

References 18 publications

Research on Different Classifiers for Early Detection of Lung Nodules

Research on Different Classifiers for Early Detection of Lung Nodules

Deep learning‐based multi‐modal computing with feature disentanglement for MRI image synthesis

A Survey on Multi-Scale Medical images Fusion Techniques: Brain Diseases

Contact Info

Product

Resources

About