PCA Based Medical Image Fusion in Ridgelet Domain

Krishn, Abhinav; Bhateja, Vikrant; Himanshi,; Sahu, Akanksha

doi:10.1007/978-3-319-12012-6_52

Cited by 15 publications

(4 citation statements)

References 41 publications

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“…These include: different noise, various image resolutions, different contrast, or misregistration of the images to mention a few . Traditional methods include morphology‐based fusion, wavelet‐based fusion, component analysis based fusion, and hybrid fusion . Different feature fusion approaches using deep neural networks are discussed: The first and most basic method is to combine the input images/features and process them jointly in a single UNET.…”

Section: Discussionmentioning

confidence: 99%

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Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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Purpose To investigate the use and efficiency of 3‐D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual‐modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)‐computed tomography (CT) images. Methods We used DFCN cosegmentation for NSCLC tumors in PET‐CT images, considering both the CT and PET information. The proposed DFCN‐based cosegmentation method consists of two coupled three‐dimensional (3D)‐UNets with an encoder‐decoder architecture, which can communicate with the other in order to share complementary information between PET and CT. The weighted average sensitivity and positive predictive values denoted as Scores, dice similarity coefficients (DSCs), and the average symmetric surface distances were used to assess the performance of the proposed approach on 60 pairs of PET/CTs. A Simultaneous Truth and Performance Level Estimation Algorithm (STAPLE) of 3 expert physicians’ delineations were used as a reference. The proposed DFCN framework was compared to 3 graph‐based cosegmentation methods. Results Strong agreement was observed when using the STAPLE references for the proposed DFCN cosegmentation on the PET‐CT images. The average DSCs on CT and PET are 0.861 ± 0.037 and 0.828 ± 0.087, respectively, using DFCN, compared to 0.638 ± 0.165 and 0.643 ± 0.141, respectively, when using the graph‐based cosegmentation method. The proposed DFCN cosegmentation using both PET and CT also outperforms the deep learning method using either PET or CT alone. Conclusions The proposed DFCN cosegmentation is able to outperform existing graph‐based segmentation methods. The proposed DFCN cosegmentation shows promise for further integration with quantitative multimodality imaging tools in clinical trials.

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Section: Discussionmentioning

confidence: 99%

“…45,46 Traditional methods include morphology-based fusion, 47 waveletbased fusion, 48 component analysis based fusion, 49 and hybrid fusion. 50 Different feature fusion approaches using deep neural networks are discussed:…”

Section: A Performancementioning

confidence: 99%

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

et al. 2019

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“…• Principal components analysis: PCA used in many works [216][217][218][219][220][221][222][223] makes it possible to carry out a linear orthogonal transformation of a multivariate set of data which contains variables correlated with N dimensions in other containing new variables not correlated to M smaller size dimensions. The transformation parameters sought are obtained by minimizing the error covariance introduced by neglecting N-M of the transformed components.…”

Section: Spatial Domain Techniquesmentioning

confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

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“…Subsequently, these components are combined to obtain a new set of main components, to which the PCA Reverse Transform is applied and thereby the fused image is finally obtained. Image fusion using PCA has two approaches [16] high image resolution value replaces PC1, which is common in all bands while the spectral information is unique to each band. The first PC1 has a maximum variance which can maximize the effect of high data resolution on image fusion.…”

Section: Figure 2: Diagram Of the Transformation Of Main Componentsmentioning

confidence: 99%

Multimodal Brain Images Fusion using Cultural Algorithm Optimized Multispectral Features

2019

IJEAT

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Medical images can be acquired through different techniques (modalities), which have their own application areas; some of them provide information on the functional activity, while others contain only anatomic information. Usually, in the first case, images have low spatial resolution while in the second case have a higher resolution. However, the analysis of medical images often requires the evaluation of more than one modality; in order provide the specialist with more information for decision making as well as for the analysis and the treatment of diseases. Image fusion aims to combine information from the same sensor or different sensors, so that the image fused retain the information content of each individual image. In remote perception, when multispectral images are analyzed, it is very important to preserve the content of spectral information of each of the bands. The challenge is to obtain good quality images that allow us to extract as much amount of information possible, for which it is sometimes necessary to enhance or modify the image to improve its appearance or combine images or portions thereof to combine the information. An ideal fusion of multispectral images and the band panchromatic will result in a new series of bands with greater spatial resolution and equal spectral content. This paper proposes a PCA, DWT and cultural optimized entropy based DWT fusion with the evaluation parameters; arithmetic mean (SM), Maximum value ( ) and Minimum value ( ).

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PCA Based Medical Image Fusion in Ridgelet Domain

Cited by 15 publications

References 41 publications

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Multimodal Brain Images Fusion using Cultural Algorithm Optimized Multispectral Features

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