Multimodal medical image fusion using PCNN optimized by the QPSO algorithm

Xiaoli, Xu; Dong, Shan; Wang, Guanying; Jiang, Xiangying

doi:10.1016/j.asoc.2016.03.028

Cited by 111 publications

(35 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As these three schemes are the conceptual abstraction of the multi-modal image analysis, most of the current literature reports can be grouped accordingly. To name a few, works in [5] (co-analysis of fMRI and EEG using CCA), [6] (co-analysis of MRI and PET using PLSR) and [7] (co-learning features through pulse-coupled NN) perform the feature-level fusion of the images. Works in [8] (using contourlet), [9] (using wavelet), [10] (using wavelet) and [11] (using features learned by LDS) perform the classifier-level fusion.…”

Section: Conceptual Design For Image Fusion Schemesmentioning

confidence: 99%

Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes

Quo

Huang

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

View full text Add to dashboard Cite

Image analysis using more than one modality (i.e. multi-modal) has been increasingly applied in the field of biomedical imaging. One of the challenges in performing the multimodal analysis is that there exist multiple schemes for fusing the information from different modalities, where such schemes are application-dependent and lack a unified framework to guide their designs. In this work we firstly propose a conceptual architecture for the image fusion schemes in supervised biomedical image analysis: fusing at the feature level, fusing at the classifier level, and fusing at the decision-making level. Further, motivated by the recent success in applying deep learning for natural image analysis, we implement the three image fusion schemes above based on the Convolutional Neural Network (CNN) with varied structures, and combined into a single framework. The proposed image segmentation framework is capable of analyzing the multi-modality images using different fusing schemes simultaneously. The framework is applied to detect the presence of soft tissue sarcoma from the combination of Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET) images. It is found from the results that while all the fusion schemes outperform the single-modality schemes, fusing at the feature level can generally achieve the best performance in terms of both accuracy and computational cost, but also suffers from the decreased robustness in the presence of large errors in any image modalities.

show abstract

Section: Conceptual Design For Image Fusion Schemesmentioning

confidence: 99%

Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes

Quo

Huang

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence, Artificial Neural Network, Fuzzy logic and expert systems have been increasingly used in various applications in the last 30 years: Engineering design, Image recognition [2]; Prediction, Estimation, Pattern recognition, and optimization [3]; Petroleum exploration and production; civil engineering, environmental and water resources engineering, traffic engineering, highway engineering, geotechnical engineering [4]; Image classification [5]; Fingerprint analysis [6]; Software defect prediction [7]; Breast cancer identification [8]; Human action recognition, video surveillance to health-care [9]; Video retrieval [10]; Localization scheme of wireless sensor networks, military surveillance, environmental monitoring, robotics, domestics, animal tracking [11]; Image recognition of plant diseases [12]; Wind power forecasting [13]; Design and analysis of antennas [14]; Image recognition [15]; Multimodal medical image fusion [16]; Satellite data and GPS [17]; water resources engineering [18]; air traffic control [19]; financial forecasting [20]; earthquake prediction [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

Narayanakumar¹,

Raja²

2016

View full text Add to dashboard Cite

The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequencymagnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the How to cite this paper: Narayanakumar, S. and Raja, K.

show abstract

“…Since nature-inspired techniques became popular in computer vision, they have been applied extensively in medical image fusion. Xu et al [29] have fused multimodal medical images by means of adaptive pulse-coupled neural networks (PCNN). They proposed automatic and optimum parameters tuning of the PCNN model by using the quantum-behaved particle swarm optimization algorithm.…”

Section: Bloodmentioning

confidence: 99%

Fusion of Intraoperative 3D B-mode and Contrast-Enhanced Ultrasound Data for Automatic Identification of Residual Brain Tumors

et al. 2017

View full text Add to dashboard Cite

Intraoperative ultrasound (iUS) imaging is routinely performed to assist neurosurgeons during tumor surgery. In particular, the identification of the possible presence of residual tumors at the end of the intervention is crucial for the operation outcome. B-mode ultrasound remains the standard modality because it depicts brain structures well. However, tumorous tissue is hard to differentiate from resection cavity borders, blood and artifacts. On the other hand, contrast enhanced ultrasound (CEUS) highlights residuals of the tumor, but the interpretation of the image is complex. Therefore, an assistance system to support the identification of tumor remnants in the iUS data is needed. Our approach is based on image segmentation and data fusion techniques. It consists of combining relevant information, automatically extracted from both intraoperative B-mode and CEUS image data, according to decision rules that model the analysis process of neurosurgeons to interpret the iUS data. The method was tested on an image dataset of 23 patients suffering from glioblastoma. The detection rate of brain areas with tumor residuals reached by the algorithm was qualitatively and quantitatively compared with manual annotations provided by experts. The results showed that the assistance tool was able to successfully identify areas with suspicious tissue.

show abstract

Multimodal medical image fusion using PCNN optimized by the QPSO algorithm

Cited by 111 publications

References 22 publications

Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes

Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

Fusion of Intraoperative 3D B-mode and Contrast-Enhanced Ultrasound Data for Automatic Identification of Residual Brain Tumors

Contact Info

Product

Resources

About