Medical Image Fusion using Content Based Automatic Segmentation (original) (raw)
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A Survey on Various Medical Image Fusion Techniques
Image fusion is a method of integrating all relevant and complementary information from images of same source or various sources into a single composite image without any degradation. Three major fusion methods have been dealt in the literature of image fusion – pixel level, feature level and decision level. In this paper, a novel pixel level fusion called Iterative block level principal component averaging fusion is proposed by dividing source images into smaller blocks, thus principal components are calculated for relevant block of source images. Analyzing quantitative and qualitative metrics such as average mutual information and mean structural similarity index clearly demonstrate that the proposed algorithm proves superior than other algorithms for the fusion of noise free and noise filtered MR images.
Image Fusion is the process of combining two or more input images to obtain a resultant image which is rich in relevant information as compared to the original input image. The fusion technique finds its application in many areas: Robot Vision, Satellite Imaging, Medical Imaging, Remote Sensing and Defense imaging. In that Medical Imaging being the prominent ones. For efficient diseases detection and treatment, images from different modalities are combined using fusion techniques. This paper describes different techniques for fusion of multimodality images and the resultant images are analyzed using different quantitative measure. Initially, three different pairs of image are taken as input: Magnetic
An automatic fusion algorithm for multi-modal medical images
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2017
Multi-modal medical image fusion provides an informative and qualitative image, which enhances the accuracy of clinical diagnosis and surgical planning. The most common and effective approaches for medical image fusion involve principal component analysis (PCA), the discrete wavelet transform and the dual-tree complex wavelet transform (DTCWT). These existing algorithms perform fusion in either the spatial or transform domains. In this paper, an advanced fusion algorithm, which combines the DTCWT with PCA, is proposed to perform fusion on several medical imaging modalities. The input images are decomposed by the DTCWT and different fusion rules are applied to combine the coefficients. While PCA is used to fuse the low frequency coefficients, the high frequency coefficients are fused using the maximum fusion rule. The main advantages of the proposed method are the use of both DTCWT and PCA methods together. The DTCWT extracts the salient information about the input images, and then the PCA method is applied to calculate the principal component based on the information on the input images instead of taking only the average value of the low frequency components. The performance of the proposed method was compared with several existing methods. The experimental results obtained reveal that our proposed fusion algorithm performs better than existing schemes both quantitatively and in terms of visual perception.
Comparative Analysis of Various Image Fusion Techniques For Biomedical Images: A Review
Image Fusion is a process of combining the relevant information from a set of images, into a single image, wherein the resultant fused image will be more informative and complete than any of the input images. This paper discusses implementation of DWT technique on different images to make a fused image having more information content. As DWT is the latest technique for image fusion as compared to simple image fusion and pyramid based image fusion, so we are going to implement DWT as the image fusion technique in our paper. Other methods such as Principal Component Analysis (PCA) based fusion, Intensity hue Saturation (IHS) Transform based fusion and high pass filtering methods are also discussed. A new algorithm is proposed using Discrete Wavelet transform and different fusion techniques including pixel averaging, min-max and max-min methods for medical image fusion.
Comparative Analysis of Medical Image Fusion
International Journal of Computer Applications, 2013
This paper explores different medical image fusion methods and their comparison to find out which fusion method gives better results based on the performance parameters. Here medical images of magnetic resonance imaging (MRI) and computed tomography (CT) images are fused to form new image. This new fused image improves the information content for diagnosis. Fusing MRI and CT images provide more information to doctors and clinical treatment planning system. MRI provides better information on soft tissues whereas CT provides better information on denser tissues. Fusing these two images gives more information than single input image. In this paper, wavelet transform, principle component analysis (PCA) and Fuzzy Logic techniques are utilized for fusing these two images and results are compared. The fusion performance is evaluated on the basis of root mean square error (RMSE), peak signal to noise ratio (PSNR) and Entropy (H).
MULTI-MODAL MEDICAL IMAGE FUSION BASED ON MULTI-BASED BINARY RESIDUAL FEATURE FUSION
AnKa Publication, 2022
The term "medical image fusion" refers to the procedure of aligning and blending separate pictures captured using different imaging technologies. By enhancing imaging quality and decreasing unpredictability and redundancy, it aims to boost the clinical usefulness of medical pictures for the diagnosis and evaluation of medical conditions. Accuracy of clinical choices based on medical pictures has been enhanced via the use of multi model medical image fusion algorithms and tools. In this study, we show that combining pictures from chest X-rays and ultrasounds improve visibility. On the other hand, the merged picture is essential for a high-quality display. Improving the quality of the combined photos requires a classification technique. The accurate fusion and integration of medical images from multiple modalities is a critical step in the diagnostic process that leads to clinical activities and the appropriate treatment of patients, and this chapter suggests a Multi-based Binary Residual Feature Fusion (MBRFF) method for this purpose. The images are preprocessed using the Histogram equalization algorithm. For the feature extraction of medical images, the Multi-Threshold Contour Method is used. The shift variant decomposition transformation technique Discrete Wavelet Transform DWT is effective and it is used to decompose the medical image. The fused medical images are optimized by using Particle swarm optimization PSO. Finally, the simulation results reveal that the suggested mechanism outperforms the other techniques based on conventional approaches. Using the Origin Pro application, the study's outcomes are represented graphically
FUSION OF MULTIMODALITY MEDICAL IMAGES BASED ON MULTISCALE DECOMPOSITION USING CONTOURLET TRANSFORM
In this paper, we propose a novel multimodality Medical Image Fusion (MIF) method based on improved Contourlet Transform (CNT) for spatially registered, multi-sensor, multiresolution medical images. The major drawback of the contourlet construction is that its basis images are not localized in the frequency domain. In this paper we propose a new contourlet construction as a solution. The source medical images are first decomposed by improved CNT. Instead of using the laplacian pyramid in contourlet transform, we employ a new multiscale decomposition defined in the frequency domain. So that the resulting basis images are sharply localized in the frequency domain and exhibit smoothness along their main ridges in the spatial domain. The low-frequency subbands (LFSs) are fused using the novel combined Activity Level Measurement and the high-frequency subbands (HFSs) are fused according to their 'local average energy' of the neighborhood of coefficients. Then inverse contourlet transform (ICNT) is applied to the fused coefficients to get the fused image. The performance, experimental results or comparison of the proposed scheme is evaluated by various quantitative measures like Mutual Information, Spatial Frequency and Entropy etc. The purpose of this paper is to replace the pyramid decomposition with multiscale decomposition to make image much smoother and to increase the efficiency of the fusion method and quality in the Image. about visceral anatomy such as CT, MRI. Multi-modality medical image fusion is to combine complementary medical image information of various modals into one image, so as to provide far more comprehensive information and improves reliability of clinical diagnosis and therapy [4-6]. Image fusion has three levels: pixel-level, feature-level and symbol-level respectively. Image fusion at pixel-level means fusion at the lowest processing level referring to the merging of the measured physical parameters and its application is very wide. Pixel-level fusion is divided into two parts, signal-level and image-point fusion. Signal-level fusion refers to synthesize a group of signals offered by sensors. The purpose is to obtain high-quality signals, which format is consistent with the original. Image points of every image are directly synthesized in the process of image-point fusion. Pixel-level fusion is operated in the phase of image pre-processing. The purpose is to obtain a further clear image, which is involved in more information. Pixel-level fusion is a low level fusion. Before fusing images, image registration of original images must be done. Because the imaging mechanisms of different from different time, different views of angle and different circumstance, the gray values and features of different images are inconsistent. So the original images must be registered at first. Image registration is the process of matching two or more three images that get from the same scene derived from different time, different sensors or different views of angle. Featurelevel fusion is done in the course of image feature extraction. It's the medium level fusion and prepared for decision-level fusion. In the process of feature-level fusion, features of every image are extracted. The typical features are edge, shape, profile, angle, texture, similar lighting area and similar depth of focus area. Decision-level fusion is the highest-level fusion. All decision and control are decided according to the results of decision-level fusion. Medical image fusion is a research focus in the academic circles. With the development of modern medical imaging technology, more and more medical image used in clinical practice [7] [8]. Nowadays, with the rapid development in high-technology and modern instrumentations, medical imaging has become a vital component of a large number of applications, including diagnosis, research, and treatment. In order to support more accurate clinical information for physicians to deal with medical diagnosis and evaluation, multimodality medical images are needed, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), and positron emission tomography (PET) images. These multimodality medical images usually provide complementary and occasionally conflicting information. For example, the CT image can provide dense structures like bones and implants with less distortion, but it cannot detect physiological changes, while the MRI IJREAS Volume 2, Issue 7 (July 2012)
Medical Image Fusion: A Brief Introduction
Biomedical and Pharmacology Journal
Digital images are an extremely powerful and widely used medium of communication. They are able to represent very intricate details about the world that surrounds us in a very easy, compact and readily available manner. Due to the innate advances in the acquisition devices such as bio-sensors and remote sensors, huge amount of data is accessible for the further processing and information extraction. The need to efficiently process this immense amount of information has given rise to the emergence of the popular disciplines like image processing, image analysis, and computer vision and image fusion. This article gives a brief insight into basic understanding and significance of image fusion.
Survey and analysis of various image fusion techniques for clinical CT and MRI images
International Journal of Imaging Systems and Technology, 2014
The research and development of biomedical imaging techniques requires more number of image data from medical image acquisition devices, like computed tomography (CT), magnetic resonance imaging (MRI), positron emission technology, and single photon emission computed tomography. Multimodal image fusion is the process of combining information from various images to get the maximum amount of content captured by a single image acquisition device at different angles and different times or stages. This article analyses and compares the performance of different existing image fusion techniques for the clinical images in the medical field. The fusion techniques compared are simple or pixel-based fusion, pyramid-based fusion, and transform-based fusion techniques. Four set of CT and MRI images are used for the above fusion techniques. The performance of the fused results is measured with seven parameters. The experimental results show that out of seven parameters the values of four parameters, such as average difference, mean difference, root mean square error, and standard deviation are minimum and the values of remaining three parameters, such as peak signal to noise ratio, entropy, and mutual information are maximum. From the experimental results, it is clear that out of 14 fusion techniques taken for survey, image fusion using dual tree complex wavelet transform gives better fusion result for the clinical CT and MRI images. Advantages and limitations of all the techniques are discussed with their experimental results and their relevance.
Improving the Information in Medical Image by Adaptive Fusion Technique
Lecture Notes in Computer Science, 2018
Image fusion plays a huge role in many fields, especially in medical image processing because the visual interpretation of the image can enhance by using the fusion technique. The result shows the important detail which is very useful for doctor to diagnose health problems. In the paper, we proposed a method for image fusion. The guided filter is used to enhance the detail of the input image and then the cross bilateral filter is applied to extract detail image from the enhanced image. The image result is made by weighted average using the weights calculated from the detailed images. The experimental results showed that the proposed method can work well with medical image as well as other kinds of image. In addition, our result is better than the other recent methods based on compared objective performance measures.