An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Bhutto, Jameel Ahmed; Li, Tian; Du, Qian; Soomro, Toufique Ahmed; Yu, Lubin; Tahir, Muhammad Faizan

doi:10.1109/access.2020.3018264

Cited by 16 publications

(13 citation statements)

References 39 publications

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“…Above all, the mapping function to a ten dimensional feature space has been designed as Eq. (6). Each pixel of the medical images is mapped using Eq.…”

Section: A Linear Spectral Clusteringmentioning

confidence: 99%

“…Spatial domain methods such as PCA [4], IHS [5], and averaging fusion select pixels from the source images to construct the final fused image. This kind of fusion methods can completely preserve spatial information and reduce computational complexity [6]. However, they also introduce color distortion and suffer from contrast decrease, which are unacceptable for the fusion of medical images.…”

Section: Introductionmentioning

confidence: 99%

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A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation

et al. 2021

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For multimodal medical image fusion problems, most of the existing fusion approaches are based on pixel-level. However, the pixel-based fusion method tends to lose local and spatial information as the relationships between pixels are not considered appropriately, which has much influence on the quality of the fusion results. To address this issue, a region-based multimodal medical image fusion framework is proposed based on superpixel segmentation and a post-processing optimization method in this paper. In this framework, the averaging image of the source medical images are firstly obtained by a weighted averaging method. To effectively obtain homogeneous regions and preserve the complete information of image details, the fast linear spectral clustering(LSC) superpixel algorithm is carried out to segment the averaging image and get superpixel labels. For each region of the medical images, log-gabor filter(LGF) and sum modified laplacian(SML) are adopted to extract texture feature and contrast feature for the measurement of region importance. The most important regions are selected and the decision map is generated by comparison. Moreover, to get a more accurate decision map, a new post-processing optimized method based on genetic algorithm(GA) is given. A weighted strategy is applied to the extracted features and the weighting factor can be adaptively adjusted by GA. The effectiveness of the proposed fusion method is validated by conducting experiments on eight pairs of medical images from diverse modalities. In addition, seven other mainstream medical image fusion methods are adopted for comparing the performance of fusion. Experimental results in terms of qualitative and quantitative evaluation demonstrate that the proposed method can achieve stateof-the-art performance for multimodal medical image fusion problems.INDEX TERMS Multimodal medical image fusion, superpixel segmentation, genetic algorithm, log-gabor filter, sum modified laplacian.

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“…Above all, the mapping function to a ten dimensional feature space has been designed as Eq. (6). Each pixel of the medical images is mapped using Eq.…”

Section: A Linear Spectral Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation

et al. 2021

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“…Content may change prior to final publication. the significant variations among the image pixels using the scores of the principal component that represents the maximum variance direction through the given pixels [38], [39]. Furthermore, PCA adds robustness against the non-uniform illumination that is common in microscopy [40], [41].…”

Section: B Phase 2: Final Wbc Detection Based On Wavelet-based Thresholding Segmentationmentioning

confidence: 99%

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

2021

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Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.

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“…Numerous image fusion schemes are developed and designed to sustain a variety of applications such as satellite imaging, medical diagnosis, object recognition and detection, and artificial neural networks. These schemes can be grouped into two major branches: spatial-domain fusion schemes such as PCA, IHS, and Averaging fusion technique [65]- [67]. The transform fusion techniques are divided into two branches: multi-resolution and multi-scale fusion techniques [48], [54], [56], [67], [71], [81].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

et al. 2021

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The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.INDEX TERMS Medical imaging, fusion process, imaging modalities, dual-tree complex wavelet transform, curvelet transform, discrete wavelet transform, principal component analysis.

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An Enhanced Image Fusion Algorithm by Combined Histogram Equalization and Fast Gray Level Grouping Using Multi-Scale Decomposition and Gray-PCA

Cited by 16 publications

References 39 publications

A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation

A Novel GA-Based Optimized Approach for Regional Multimodal Medical Image Fusion With Superpixel Segmentation

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

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