Active Contour Extension Basing on Haralick Texture Features, Multi-gene Genetic Programming, and Block Matching to Segment Thyroid in 3D Ultrasound Images

Benabdallah, F. Z.; Djerou, Leila

doi:10.1007/s13369-022-07286-3

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…Haralick, which was initially proposed as a method to quantify the relationships between adjacent pixels in images, has found extensive applications in image processing [19] and computer vision [20], among other multidimensional data classification domains. Recently, it has gained significant traction in the field of medicine [21,22].…”

Section: B Feature Extraction 1) Haralick Featurementioning

confidence: 99%

Multi-Modal Fusion for Multi-Task Fuzzy Detection of Rail Anomalies

Liyuan,

Osman,

Abdul Rahman

et al. 2024

IEEE Access

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Due to prolonged exposure to heavy train loads, various anomalies can emerge on the surface of railway tracks, posing a direct threat to safe train operation. The accurate and timely detection of these anomalies is important to ensure safe transportation and advancing intelligent maintenance. However, in the domain of anomaly detection, several challenges have arisen owing to the variability in illumination conditions, imaging blur inherent to the capture devices, and the introduction of noise from environmental factors such as dust particles. These interferences have significantly undermined the accuracy of rail anomaly detection based on target detection techniques. In response to these challenges, this study introduced a novel approach for rail anomaly detection in the presence of image artifacts by utilizing a multi-modal multi-task framework. The objective of this study is to enhance the performance of rail anomaly detection under interference-prone conditions. This study integrated color moment features, HU invariant moment features, and Haralick features to construct a fuzzy detection model for rail anomalies using a multi-task learning (MTL) strategy. The model prioritized the primary task of classifying rail anomalies, with interference level classification and fuzzy logic interference judgment serving as auxiliary tasks within the network. Finally, based on the results of fuzzy logic and interference level detection, a fuzzy judgment was made distinguishing between "with interference" and "without interference." Experimental findings consistently demonstrated that the integration of multi-modal features and multi-task learning methodologies significantly improves the accuracy of rail anomaly recognition in the presence of interference, thus establishing an effective approach for rail anomaly identification in challenging scenarios.

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Section: B Feature Extraction 1) Haralick Featurementioning

confidence: 99%

Multi-Modal Fusion for Multi-Task Fuzzy Detection of Rail Anomalies

Liyuan,

Osman,

Abdul Rahman

et al. 2024

IEEE Access

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“…In 2018, Kuchekar [12] and others used edge detection technology to accurately extract features such as the shape, size, and aspect ratio of rice grains in a single background, classifying rice grains based on these features. In 2020, Qu [13] and others, focusing on thyroid nodules in CT images, applied homomorphic filtering for noise reduction and utilized an improved Canny operator for edge detection. Homomorphic filtering is a frequency-domain filtering technique that can simultaneously handle the brightness and contrast information of an image, showing good results in noise removal and enhancing details in CT images.…”

Section: Medical Image Segmentation Algorithms Based On Traditional I...mentioning

confidence: 99%

Enhanced Pediatric Thyroid Ultrasound Image Segmentation Using DC-Contrast U-Net

Peng,

Lin,

Zhou

et al. 2024

Preprint

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The examination methods for the thyroid include laboratory tests and imaging studies. Although laboratory and imaging examinations are relatively straightforward, their effectiveness in detecting early clinical symptoms of the thyroid may be limited, especially in children due to the shorter growth time of the pediatric thyroid. Therefore, this constitutes a crucial foundational work. However, accurately determining the position and size of the thyroid in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is significant variation in thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice.

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