Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

Autor: Sakshi Arora, Insha Majeed Wani
Rok vydání: 2020
Předmět:
Male
Computer science
Osteoporosis
CAD
02 engineering and technology
computer.software_genre
030218 nuclear medicine & medical imaging
Machine Learning
Absorptiometry
Photon
0302 clinical medicine
Bone Density
Surveys and Questionnaires
Segmentation
Diagnosis
Computer-Assisted
Medical diagnosis
Ultrasonography
Aged
80 and over
Middle Aged
Magnetic Resonance Imaging
Computer Science Applications
Fractals
Female
Algorithms
Adult
Finite Element Analysis
0206 medical engineering
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Biomedical Engineering
Image processing
Machine learning
03 medical and health sciences
Deep Learning
Fuzzy Logic
Artificial Intelligence
Region of interest
Image Interpretation
Computer-Assisted
medicine
Humans
Aged
business.industry
medicine.disease
020601 biomedical engineering
Computer-aided diagnosis
Neural Networks
Computer
Artificial intelligence
Tomography
X-Ray Computed
business
computer
Zdroj: Medical & Biological Engineering & Computing. 58:1873-1917
ISSN: 1741-0444
0140-0118
DOI: 10.1007/s11517-020-02171-3
Popis: Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje