Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms.
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http://dx.doi.org/10.1109/IEMBS.2005.1615986 | DOI Listing |
J Ultrasound
January 2025
, Costa Contina street n. 19, 66054, Vasto, Chieti, Italy.
Aim: o point out how novel analysis tools of AI can make sense of the data acquired during OL and OC diagnosis and treatment in an effort to help improve and standardize the patient pathway for these disease.
Material And Methods: ultilizing programmed detection of heterogeneus OL and OC habitats through radiomics and correlate to imaging based tumor grading plus a literature review.
Results: new analysis pipelines have been generated for integrating imaging and patient demographic data and identify new multi-omic biomarkers of response prediction and tumour grading using cutting-edge artificial intelligence (AI) in OL and OC.
ACS Appl Mater Interfaces
January 2025
School of Physics, Beihang University, Beijing 100191, China.
Exploiting biomimetic perception of invisible spectra in flexible artificial human vision systems (HVSs) is crucial for real-time dynamic information processing. Nevertheless, the fast processing of motion objects in natural environments poses a challenge, necessitating that these artificial HVSs simultaneously have swift photoresponse and nonvolatile memory. Here, inspired by the human retina, we propose a flexible UV neuromorphic visual synaptic device (NeuVSD) based on GaO@GaN-composited nanowires for dynamic visual perception.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Department of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, Egypt.
Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.
Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed.
Chem Commun (Camb)
January 2025
College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Biointerface sensing is a cutting-edge interdisciplinary field that merges conceptual and practical aspects. Wearable bioelectronics enable efficient interaction and close contact with biological components such as tissues and organs, paving the way for a wide range of medical applications, including personal health monitoring and medical intervention. To be applicable in real-world settings, the patches must be stable and adhere to the skin without causing discomfort or allergies in both wet and dry conditions, as well as other desirable features such as being ultra-soft, thin, flexible, and stretchable.
View Article and Find Full Text PDFSci Rep
January 2025
Human-Computer Collaborative Robot Joint Laboratory of Anhui Province, Huainan, China.
To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function.
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