Statistical Measurements of Multi Modal MRI – PET Medical Image Fusion using 2D – HT in HSV color Space

Haribabu, Maruturi; Guruvaiah, Velmathi

doi:10.1016/j.procs.2020.01.090

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Then, the feature vector "Clolor_features" was defined as RGB_G_FM, YCbCr_Y_FM, Lab_L_FM, RGB_G_SM, YCbCr_Y_SM, Lab_L_SM, RGB_G_TM, YCbCr_Y_TM, and Lab_L_TM, which contains nine components. As can be seen from Figure 4, since the HSV color space is composed of hue, saturation, and lightness [20], none of its channels can be used to identify the diseased part. The G channel in RGB (Figure 4b), Y channel in YCbCr (Figure 4g), and L channel in Lab (Figure 4j) can better distinguish the diseased part from the other parts; as for the remaining channels, the diseased parts were similar to the normal ones in color, and the boundaries were blurred, which cannot be used for identification.…”

Section: Color Feature Analysismentioning

confidence: 99%

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

Miao,

Wu,

Yang

et al. 2023

Applied Sciences

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Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products.

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Section: Color Feature Analysismentioning

confidence: 99%

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

Miao,

Wu,

Yang

et al. 2023

Applied Sciences

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“…In order to avoid the coefficient insensitivity of the discrete wavelet transform (DWT) algorithm in the process of image processing [21], a dual-tree complex wavelet transform (DT-CWT) algorithm is introduced. Figure 3 shows the process of algorithm transformation.…”

Section: Image Enhancement Eorymentioning

confidence: 99%

The Use of Scientific Programming Method under Visual Communication Design Concept in Urban Landscape Design

Huang

Wang

2022

Scientific Programming

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The purpose of this study is to provide more orderly and convenient urban planning ideas for the fast-paced life in the complex urban environment. The current sign design is analyzed according to the needs of urban residents for barrier-free sign design, and the sign design based on urban space color is established. An urban landscape visual communication optimization method is proposed based on hue saturation value (HSV) technology. The multiscale retinex (MSR) algorithm is used as a reference for simulation experiments. The experimental results show that the designed optimization method is significantly better than the traditional method in the expression effect of visual communication. First, the attention time of the sign design can be reduced by more than 3 seconds, which can effectively improve the lives of urban residents and tourists and improve their browsing efficiency. Next, 94% of the citizens believe that optimized urban signs are more prominent than traditional ones. Finally, the sign design optimization method proposed provides an image with a higher definition than the traditional sign design method. The proposed sign design and optimization scheme can effectively coordinate the relationship among urban landscape design, guided objects, and cities, help busy urban life, and provide new ideas for the development direction of visual communication design.

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“…Chen et al [20] proposed a method of medical image fusion that is based on Rolling Guidance Filtering. Haribabu et al [21] showed statical measurements of fusing medical images for MRI-PET images using 2D Herley transform with HSV color space. Manchanda et al [22] improved an algorithm of medical image fusion by using fuzzy transformation (FTR).…”

Section: Introductionmentioning

confidence: 99%

Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

Trung¹,

Ngân²,

Tuấn³

et al. 2023

Computer Systems Science and Engineering

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Fusing medical images is a topic of interest in processing medical images. This is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis accuracy. This fusion aims to improve the image quality and preserve the specific features. The methods of medical image fusion generally use knowledge in many different fields such as clinical medicine, computer vision, digital imaging, machine learning, pattern recognition to fuse different medical images. There are two main approaches in fusing image, including spatial domain approach and transform domain approachs. This paper proposes a new algorithm to fusion multimodal images. This algorithm is based on Entropy optimization and the Sobel operator. Wavelet transform is used to split the input images into components over the low and high frequency domains. Then, two fusion rules are used for obtaining the fusing images. The first rule, based on the Sobel operator, is used for high frequency components. The second rule, based on Entropy optimization by using Particle Swarm Optimization (PSO) algorithm, is used for low frequency components. Proposed algorithm is implemented on the images related to central nervous system diseases. The experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level, the contrast, the entropy, the gradient and visual information fidelity for fusion (VIFF), Feature Mutual Information (FMI) indices.

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Statistical Measurements of Multi Modal MRI – PET Medical Image Fusion using 2D – HT in HSV color Space

Cited by 7 publications

References 7 publications

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

The Use of Scientific Programming Method under Visual Communication Design Concept in Urban Landscape Design

Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

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