A novel multimodal medical image fusion using sparse representation and modified spatial frequency

Aishwarya, N.; Thangammal, C. Bennila

doi:10.1002/ima.22268

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…In this table, three parameters, sensitivity, specificity, and accuracy, are used for evaluation purposes. The quantitative results showed that the purposed technique outperformed the U-Net [37], AWEU-Net [38], 2D U-Net [9], 2D Seg U Det [39], 3D FCN [40], 3D nodule R-CNN [41], 2D AE [42], 2D CNN [43], 2D LGAN [44], and 2D encoder-decoder [45]. The accuracy of purposed method was 99%, which is much better than the other listed methods, as shown in Table 2.…”

Section: Image Segmentationmentioning

confidence: 91%

“…Aishwarya and Thangammal [9] suggested a multi-modal medical image fusion adaptive dictionary learning algorithm. Useful information blocks were isolated for dictionary learning by removing zero information blocks and estimating the remaining image patches with a Modified Spatial Frequency (MSF).…”

Section: Dictionary Learningmentioning

confidence: 99%

“…Liu et al [8] suggested ASR for both image fusion and denoising, which can adaptively create a compressed dictionary for the fusion of images. Aishwarya et al [9] applied the adjusted spatial frequency to image fusion and tried to introduce the basic concept of an adaptive selection dictionary to SR. In 2015, Singh and Khare [10] investigated an image fusion method for multi-view medical images based on two redundant wavelet transforms (Redundant Wavelet Transform (RWT) and R-DWT).…”

Section: Introductionmentioning

confidence: 99%

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Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

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Section: Image Segmentationmentioning

confidence: 91%

Section: Dictionary Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

et al. 2021

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“…The sparse representation model has also been widely used in several image fusion applications [32][33][34][35]. A comprehensive review of this group is given in [36].…”

Section: B Dictionary Learning-based Methodsmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

2020

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Medical image fusion techniques primarily integrate the complementary features of different medical images to acquire a single composite image with superior quality, reducing the uncertainty of lesion analysis. However, the simultaneous extraction of more salient features and less meaningless details from medical images by using multi-scale transform methods is a challenging task. This study presents a two-scale fusion framework for multimodal medical images to overcome the aforementioned limitation. In this framework, a guided filter is used to decompose source images into the base and detail layers to roughly separate the two characteristics of source images, namely, structural information and texture details. To effectively preserve most of the structural information, the base layers are fused using the combined Laplacian pyramid and sparse representation rule, in which an image patch selection-based dictionary construction scheme is introduced to exclude the meaningless patches from the source images and enhance the sparse representation capability of the pyramid-decomposed low-frequency layer. The detail layers are subsequently merged using a guided filtering-based approach, which enhances contrast level via noise filtering as much as possible. The fused base and detail layers are reconstructed to generate the fused image. We experimentally verify the superiority of the proposed method by using two basic fusion schemes and conducting comparison experiments on nine pairs of medical images from diverse modalities. The comparison of the fused results in terms of visual effect and objective assessment demonstrates that the proposed method provides better visual effect with an improved objective measurement because it effectively preserves meaningful salient features without producing abnormal details. INDEX TERMS Medical image fusion, guided filtering, sparse representation, salient features, meaningless details.

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“…The basic principle of location method based on priori model is to establish a standard model which can be used to segment new medical images on the basis of many years of research experience and rich pathological knowledge of medical experts. Compared with the method based on image features, the speed and accuracy of the method based on priori model are improved considerably [9,10].…”

Section: Introductionmentioning

confidence: 99%

Location detection of key areas in medical images based on Haar-like fusion contour feature learning

Wang

et al. 2020

THC

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BACKGROUND: Key area location is an important content of medical image processing and an important detail of auxiliary medical diagnosis. OBJECTIVE: In this paper, a prior knowledge fusion method based on Haar-like feature and contour feature is proposed to locate and detect key areas in medical images. METHOD: For the image to be processed, six Haar-like features and five contour features are extracted respectively. The improvement of Haar-like feature extraction template better adapts to the complexity of regional structure of medical images. The design of the contour feature extraction process fully reflects the consideration of feature invariance. The two features, together with prior knowledge, are fed into their respective decision makers and final fusers as the basis for determining and locating key regions. RESULTS: The experimental results show that the proposed method has excellent performance in locating key regions of medical images on MRI. When the capacity of the database increases from 10 to 200, the accuracy of locating the key areas of the image to be processed still reaches more than 90%. CONCLUSION: The proposed method realizes the accurate location of the key areas of medical images, which is of great significance for the auxiliary medical diagnosis.

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A novel multimodal medical image fusion using sparse representation and modified spatial frequency

Cited by 17 publications

References 28 publications

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

Location detection of key areas in medical images based on Haar-like fusion contour feature learning

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