Computational advances applied to medical image processing: an update

João, Ana; Gambaruto, Alberto; Tiago, Jorge; Sequeira, Adélia

doi:10.2147/oab.s70327

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The selection of the constant ζ is influenced by two factors: it should be sufficiently small to ensure the convergence of the solution, and large enough to satisfy the requirements for the choice of structural element scale. The selection of ζ can refer to the stopping criterion for anisotropic diffusion defined by Sequeira et al 34 .…”

Section: Aict-dmth Methodsmentioning

confidence: 99%

Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation

He,

Kang,

et al. 2024

Sci Rep

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Enhancing infrared images is essential for detecting wind turbine blades using infrared technology. This paper introduces an Infrared Image Enhancement Method based on Adaptive Iterative Cutoff Threshold Difference Multi-Scale Top-Hat Transformation (AICT-DMTH) to address the challenge of low image clarity in infrared detection. The method involves performing a black-white difference top-hat transformation by utilizing structural elements of varying scales for dilation and erosion. Additionally, an iterative threshold method is applied to extract more detailed image features, followed by setting a cutoff constant to determine the final scale of the structural element. The effectiveness of the proposed method is evaluated both qualitatively and quantitatively, with infrared images from laboratory and wind farm settings enhanced and compared against existing methods. The experimental results indicate that the proposed method significantly improves the clarity of infrared images, demonstrating robustness in enhancing images from various environments.

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Section: Aict-dmth Methodsmentioning

confidence: 99%

Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation

He,

Kang,

et al. 2024

Sci Rep

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“…Many techniques have been proposed to improve the contrast of medical images [2]. The traditional histogram equalization (HE) [3], one of the most popular techniques, was the first attempt to automatically improve contrast.…”

Section: Introductionmentioning

confidence: 99%

Dermoscopy Images Enhancement via Multi-Scale Morphological Operations

et al. 2021

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Skin dermoscopy images frequently lack contrast caused by varying light conditions. Indeed, often low contrast is seen in dermoscopy images of melanoma, causing the lesion to blend in with the surrounding skin. In addition, the low contrast prevents certain details from being seen in the image. Therefore, it is necessary to design an approach that can enhance the contrast and details of dermoscopic images. In this work, we propose a multi-scale morphological approach to reduce the impacts of lack of contrast and to enhance the quality of the images. By top-hat reconstruction, the local bright and dark features are extracted from the image. The local bright features are added and the dark features are subtracted from the image. In this way, images with higher contrast and detail are obtained. The proposed approach was applied to a database of 236 color images of benign and malignant melanocytic lesions. The results show that the multi-scale morphological approach by reconstruction is a competitive algorithm since it achieved a very satisfactory level of contrast enhancement and detail enhancement in dermoscopy images.

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“…Medical image segmentation concerns both detection and delineation of anatomical or physiological structures from the background, distinguishing among the different components included in the image [1]. This important task allows for the extraction of clinically useful information and features in medical im-age analysis [2,3]. Accordingly, computer-assisted approaches enable quantitative imaging [4], whose aim is to derive accurate and objective measurements from digital images regarding a Region of Interest (ROI) [5,6].…”

Section: Introductionmentioning

confidence: 99%

A novel framework for MR image segmentation and quantification by using MedGA

Rundo

Tangherloni

Cazzaniga

et al. 2019

Computer Methods and Programs in Biomedicine

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Background and Objectives: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework

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Computational advances applied to medical image processing: an update

Cited by 3 publications

References 16 publications

Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation

Advanced enhancement technique for infrared images of wind turbine blades utilizing adaptive difference multi-scale top-hat transformation

Dermoscopy Images Enhancement via Multi-Scale Morphological Operations

A novel framework for MR image segmentation and quantification by using MedGA

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