An improved multimodal medical image fusion algorithm based on fuzzy transform

Manchanda, Meenu; Sharma, Rajiv Kumar

doi:10.1016/j.jvcir.2017.12.011

Cited by 81 publications

(19 citation statements)

References 44 publications

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“…However, the latter requires greater attention as enhanced clinical diagnosis remains the top priority around the world [6]. In this regard, clinical imaging plays a vital role in modern day health care systems, where Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are among the most extensively used imaging modalities [7][8][9]. This allows radiologists to analyze the human body and generate different patterns, which are used in clinical analysis [10].…”

Section: Introductionmentioning

confidence: 99%

“…This allows radiologists to analyze the human body and generate different patterns, which are used in clinical analysis [10]. These images provide anatomical statistics [7]; however, the extraction of purposeful functional details from an individual image remains a critical issue. This demands multimodal image fusion, which integrates the complementary information of images from different modalities to produce an enhanced fused image through simulation, thereby providing enriched anatomical and functional information [6,7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…There are several multimodal image fusion algorithms [7,14,15], which are divided into two major classes, namely the spatial and transform domains [2]. The spatial domain produces fused images based on the coefficient deduced from pixels, sections, or blocks without transformation [14,16], which produces false structural artifacts in the resulting fused images.…”

Section: Introductionmentioning

confidence: 99%

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CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

et al. 2020

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Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

et al. 2020

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“…Image fusion must be more useful for further machine processing [2]. Computer assisted diagnoses and therapy are of increasing importance in health care [3]. Medical image fusion plays a very significant role in performing accurate and early diagnosis, dosage measurement in medical dosimetry, precise tumor location and lesion identification.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Image Fusion in Clinical Research

Kaur*¹,

Singh²,

Vig³

2019

IJRTE

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With the advancement in medical imaging, far reaching changes are perceptible in clinical analysis. Most of the diagnosed medical evaluation is a resultant of imaging or it is in close synchronization with imaging techniques. This promotes the need to closely read, evaluate and collaborate images. Medical image fusion is a technique to collaborate the clinical images acquired from one or many modalities. Multimodal image analysis during fusion well capitalizes the strength of each medical modality. Incorporating the features from multimodal input images thus holds added potential to abet better-quality diagnosis. Medical image fusion is an intricate task specially when high quality fused image, possessing all relevant information and reasonable operating speed is aimed. Many efforts have been undertaken in this field resulting in diverse research approaches. Image fusion can be performed using medical images obtained from single modality or from multiple modalities. This paper has been designed collaborating the work based on multimodal medical images in multiscale image fusion domain. When the same region, organ or a tissue is captured from various different perceptions, complimentary information is maximized and diagnostic value is reinforced. The fusion framework using Mexican Hat wavelet is proposed using adaptive median filtering detailing each executable fusion block. The Fusion techniques, pre and post processing aspects and evaluation mechanisms are illustrated from literature. The drifts of researchers from single processing to multiple processing hybrid techniques are discussed. The medical modality aspects are detailed. These may provide as a valuable reference to understand the image fusion trade-offs comprehensively with future viabilities.

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“…The fused image is finally obtained using inversetransformation of the fusion result. Among the various transform-domain fusion methods, pyramid-transformbased image fusion methods [8] and wavelet-transform-*For correspondence based image fusion methods [9,10] are the most popular methods. However, pyramid transforms fail in providing spatial orientation selectivity during the decomposition process, thereby resulting in blocking artefacts in the reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

Multifocus image fusion based on waveatom transform

Manchanda¹,

Gambhir

2019

Sādhanā

Self Cite

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Multifocus image fusion has emerged as a challenging research area due to the availability of various image-capturing devices. The optical lenses that are widely utilized in image-capturing devices have limited 'depth-of-focus' and, therefore, only the objects that lie within a particular depth remain 'in-focus', whereas all the other objects go 'out-of-focus'. In order to obtain an image where all the objects are well focused, multifocus image fusion method based on waveatom transform is proposed. The core idea is to decompose all input images using waveatom transform and perform fusion of resultant waveatom coefficients. The waveatom coefficients with higher visibility, corresponding to sharper image intensities, are used to perform the process of image fusion. Finally, the fused image is obtained by performing inverse waveatom transform. The performance of the proposed method is demonstrated by performing fusion on different sets of multifocus images and comparing the results of the proposed method to the results of existing image fusion methods.

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An improved multimodal medical image fusion algorithm based on fuzzy transform

Cited by 81 publications

References 44 publications

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

Multimodal Image Fusion in Clinical Research

Multifocus image fusion based on waveatom transform

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