Multi-modal image fusion technique for enhancing image quality with multi-scale decomposition algorithm

Sunitha, T.; Rajalakshmi, R.

doi:10.1080/21681163.2020.1830437

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Conventional histogram equalization adjusts the contrast of an image by modifying the intensity distribution of the histogram. This strategy usually rises the global contrast of many images, especially when the usable data of the image is represented by close contrast values [40]. Through this adjustment, the intensities can be better distributed on the histogram.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.

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Section: Histogram Equalizationmentioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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Application of Multi-Focused and Multimodal Image Fusion Using Guided Filter on Biomedical Images

Jagtap¹,

Thepade²

2022

4th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

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A novel fusion method for X-ray phase contrast imaging based on fast adaptive bidimensional empirical mode decomposition

Tian,

Tao,

Bai

et al. 2023

XST

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BACKGROUNDS: X-ray phase contrast imaging (XPCI) can separate the attenuation, refraction, and scattering signals of the object. The application of image fusion enables the concentration of distinctive information into a single image. OBJECTIVE: To explore the application value of a novel image fusion method for a XPCI system and a computed tomography (CT) system. METHODS: The means of fast adaptive bidimensional empirical mode decomposition (FABEMD) is considered for image decomposition to avoid unnecessary information loss. A parameter δ is proposed to guide the fusion of bidimensional intrinsic mode functions, which contain high-frequency information, using a pulse coupled neural network with morphological gradients (MGPCNN). The residual images are fused by the energy attribute fusion strategy. Image preprocessing and enhancement are performed on the result to ensure its quality. RESULTS: The δ-guided FABEMD-MGPCNN method achieved favorable results with biological samples, as compared to traditional image fusion methods. Moreover, comparisons are made with other fusion methods used for XPCI. Finally, the proposed method applied in CT shows the expected results. CONCLUSIONS: The proposed δ-guided FABEMD-MGPCNN method shows potential feasibility and superiority over other traditional and recent image fusion methods for X-ray differential phase contrast imaging and computed tomography systems.

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Multi-modal image fusion technique for enhancing image quality with multi-scale decomposition algorithm

Cited by 3 publications

References 22 publications

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Application of Multi-Focused and Multimodal Image Fusion Using Guided Filter on Biomedical Images

A novel fusion method for X-ray phase contrast imaging based on fast adaptive bidimensional empirical mode decomposition

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