Multimodal medical image fusion review: Theoretical background and recent advances

Sensory data are generally associated with imprecision and uncertainty, and consequently, it becomes difficult to extract useful information from them. The problem becomes even more difficult to handle, when the data are collected using multiple sensors. Realizing the ability of fuzzy sets to deal with imprecision and uncertainty, a multi-sensors data fusion technique was developed in this study by using fuzzy clustering and predictive tools. The data were first clustered based on their similarity using an entropy-based fuzzy C-means clustering technique and the obtained clusters were utilized to develop a fuzzy reasoning-based predictive tool. The novelty of this study lies with the application of a clustering algorithm, which can ensure both compactness and distinctness of the developed clusters, and development of a reasoning tool utilizing the information of obtained clusters. Two types of multi-sensors data were used to test the performance of the proposed algorithm. Results were compared with those available in the literature, and the developed technique was found to perform better than the previous approaches on both the data sets. The better performance of the proposed algorithm could be due to its in-depth search of the data set through similarity-based fuzzy clustering followed by the development of fuzzy reasoning tool utilizing the information of obtained clusters.

Multi-sensors data fusion through fuzzy clustering and predictive tools

Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.

/pdf/current-research-opportunities-of-image-processing-and-fyb69tw391.pdf

Current research opportunities of image processing and computer vision

Medical imaging requires capturing different aspects of a human body. To capture tissues, cartilages, bones and nerves one needs different sensors and different modalities which results in different images from each modality of the same body part. Image fusion is a process by which one can fuse such images from different modalities in a single image. In this paper, a novel DWT-type2 fuzzy method is proposed to fuse two images (CT and MRI images). In this method, initially, the source images are decomposed into low-level and high-level subbands by discrete wavelet transformation (DWT). As the second step, for fusion, Type-2 fuzzy technique is applied on a low-level subband and average fusion method is applied on the high-level subbands in order to enhance the most prominent features present in CT image and MRI image. Finally, the fused low-level subband and highlevel subbands are reconstructed to form the final fused image using inverse-DWT. To test the proposed fusion method, the experiment is carried out on the MRI-T2 image, functional MRI image and CT image. The fused images have been subjectively and objectively evaluated based on certain evaluation measures and the obtained results have been presented in the paper. From the results, one can observe that the proposed method provides an improvement over other primitive fusion methods.

CT and MRI Image Fusion Based on Discrete Wavelet Transform and Type-2 Fuzzy Logic

Image fusion aims to integrate complementary information from multiple modalities into a single image with none distortion and loss of data Image fusion is important in medical imaging, specifically for the purpose of detecting the tumor and identification of diseases In this article, completely unique discrete wavelet transform (DWT) and intuitionistic fuzzy sets (IFSs) based fusion method (DWT-IFS) is proposed For fusion, initially, all source images are fused using DWT with the average, maximum, and entropy fusion rules Besides, on the fused image IFS is applied In the IFS process images are converted into intuitionistic fuzzy images (IFIs) by selecting an optimum value for the parameter in membership, non-membership, and hesitation degree function using entropy Then, the resulting IFIs are decomposed into the blocks, and the corresponding blocks of the images are fused using the intersection and union operations of IFS The efficiency of the proposed DWT-IFS fusion method is recognized by examining it with other existing methods, such as Averaging (AVG), Principal Component Analysis (PCA), Laplacian Pyramid Approach (LPA), Contrast Pyramid Approach (CPA), Discrete Wavelet Transform (DWT), Morphological Pyramid Approach (MPA), Redundancy Discrete Wavelet Transform (RDWT), Contourlet Transform (CONTRA), and Intuitionistic Fuzzy Set (IFS) using subjective and objective performance evaluation measures The experimental results reveal that the proposed DWT-IFS fusion method provides higher quality of information in terms of physical properties and contrast as compared to the existing methods

An Efficient DWT and Intuitionistic Fuzzy Based Multimodality Medical Image Fusion

Medical image fusion is the process of combining two different modality images into single image. This resultant image is helpful in medical field for efficient disease diagnoses, retrieval of images, undergo surgery treatment, tumor identification etc. This single image features cannot be obtained from the single modality medical images and it can be resolved by image fusion. This paper has proposed two fusion techniques Iterative Neuro-Fuzzy Approach (INFA), Lifting Wavelet Transform and Neuro-Fuzzy Approach (LWT-NFA). By using proposed techniques, Computed Tomography (CT) and Magnetic Resonance Image (MRI) images are fused and it has been compared with existing technique using quantitative and qualitative measures. From the implementation observed that INFA algorithm provides clear image information based on the measures.

Puja Bardhan

Papers

CT and MRI image fusion based on Wavelet Transform and Neuro-Fuzzy concepts with quantitative analysis