Determination of Hue Saturation Value (HSV) color feature in kidney histology image

Kurniastuti, Ima; Yuliati, E N I; Yudianto, Firman; Wulan, Tri Deviasari

doi:10.1088/1742-6596/2157/1/012020

Cited by 14 publications

(14 citation statements)

References 10 publications

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“…According to the findings of the sensitivity analysis, it can be concluded that the S and H were the most and least influential factors, corresponding to predict TSS and pH for both models. Some published articles also reported similar results on the importance of colour channels in object detection [89,90].…”

Section: Sensitivity Analysis Of Colour Spacessupporting

confidence: 53%

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Basak

Madhavi

Paudel

et al. 2022

Foods

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Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.

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Section: Sensitivity Analysis Of Colour Spacessupporting

confidence: 53%

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Basak

Madhavi

Paudel

et al. 2022

Foods

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“…This model finds wide applications across various scientific and technological domains, including medical research. For instance, it has been employed in the analysis of kidney histology images [ 29 ], in the study of patients undergoing treatment for ischemic heart disease to assess tongue appearance [ 30 ], and as a foundational approach for comprehensive image analysis in medicine [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…Of the existing color systems, including RGB (Red, Green, Blue), HSV/HSB (Hue, Saturation, Value / Hue, Saturation, Brightness), and CIELAB (International Commission on Illumination), the HSV/HSB model was selected for our analysis. The HSV color space is extensively applied in image retrieval contexts, aligns with human visual sensation, and enables the robust characterization of image content [ 28 , 29 , 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

Red/Orange Autofluorescence in Selected Candida Strains Exposed to 405 nm Laser Light

Wiench,

Paliga,

Mertas

et al. 2024

Dentistry Journal

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Background: Candida albicans and similar species are significant pathogens in immunocompromised and hospitalized individuals, known for mucosal colonization and bloodstream/organ invasion. Many pathogenic fungi, including these species, exhibit autofluorescence (R/OF) under specific light conditions, a feature crucial for their detection. Aim: We investigated the use of a 405 nm diode laser for the direct observation of red/orange autofluorescence of Candida spp., common in the oral cavity, exploring its potential in health screenings. Methods: This study utilized cultures of Candida spp. on Sabouraud dextrose agar with Qdot 655 and 685 for fluorescence benchmarking, illuminated using a 405 nm diode laser (continuous wave, power 250 mW, 0.0425 J/cm² fluence, 0.0014 W/cm² power density). Images were captured using a yellow-filter camera at set intervals (48 to 144 h). Visual and computational analyses evaluated the R/OF in terms of presence, intensity, coloration, and intra-colony variation. Results: Most Candida strains displayed red/orange autofluorescence at all observation times, characterized by varied coloration and intra-colony distribution. Initially, there was an increase in R/OF intensity, which then stabilized in the later stages of observation. Conclusions: The majority of the Candida strains tested are capable of emitting R/OF under 405 nm laser light. This finding opens up new possibilities for integrating R/OF detection into routine dental screenings for Candida spp.

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“…Saturation/Value mean for an HSV image is calculated by simply taking the maximum and minimum value of saturation/value of an image [60]. The basic value of skewness can be estimated by equation ( 6) below.…”

Section: Skewnessmentioning

confidence: 99%

Object-based Classification of Natural Scenes Using Machine Learning Methods

Jasim¹,

Younis²

2023

Technium

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The replication of human intellectual processes by machines, particularly computer systems, is known as artificial intelligence (AI). AI is an intelligent tool that is utilized across sectors to improve decision making, increase productivity, and eliminate repetitive tasks. Machine learning (ML) is a key component of AI since it includes understanding and developing ways that can learn or improve performance on tasks. For the last decade, ML has been applied in computer vision (CV) applications. In computer vision, systems and computers extract meaningful data from digital videos, photos, and other visual sources and use that information to conduct actions or make suggestions. In this work, we have solved the image segmentation problem for the natural images to segment out water, land, and sky. Instead of applying image segmentation directly to the images, images are pre-processed, and statistical and textural features are then passed through a neural network for the pixel-wise semantic segmentation of the images. We chose the 5X5 window over the pixel-by-pixel technique since it requires less resources and time for training and testing.

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Determination of Hue Saturation Value (HSV) color feature in kidney histology image

Cited by 14 publications

References 10 publications

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models

Red/Orange Autofluorescence in Selected Candida Strains Exposed to 405 nm Laser Light

Object-based Classification of Natural Scenes Using Machine Learning Methods

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