Automated skull damage detection from assembled skull model using computer vision and machine learning
| Autor: | Santosh B. Rane, Vivek Sunnapwar, Amol Mangrulkar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Image quality Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Normalization (image processing) Machine learning computer.software_genre Convolutional neural network Artificial Intelligence Region of interest Histogram medicine Computer vision Electrical and Electronic Engineering Geometric data analysis business.industry Applied Mathematics Deep learning Computer Science Applications Skull medicine.anatomical_structure Computational Theory and Mathematics Artificial intelligence business computer Information Systems |
| Zdroj: | International Journal of Information Technology. 13:1785-1790 |
| ISSN: | 2511-2112 2511-2104 |
| DOI: | 10.1007/s41870-021-00752-5 |
| Popis: | In the biomedical domain, the technologies like 3D computer vision and Bio-CAD arriving significant attention for computerized diagnosis, analysis and treatment of head and neck fractures. The advanced medical scanning devices internally scan and assemble the fragmented geometric data of the human body. The assembled skull model frequently suffers from damages caused by the process of the skull assembly process. Such damaged skull data may lead to missing some vital data for further medical analysis. Thus it is necessary to have an automatic mechanism of skull prototyping or completion before detect damaged skull models and repair them automatically for medical investigation. Automatic skull damage detection approach proposed using computer vision and machine learning methods in this paper. The input skull model in 3D format converted into 2D followed by the pre-processing operation to denoise and enhance the image quality. Then the Region of Interest (ROI) performed a dynamic binary segmentation technique. The automatic and manual features extracted from ROI using Convolutional Neural Network (CNN) layers and hybrid methods respectively. The hybrid model includes the structural, regional, and histogram features followed by its concatenation and normalization. The hybrid feature set is feed to conventional machine learning methods for skull damage detection. Automatic damage detection in input skull image is performed by the consolidated deep learning model using CNN (For features extraction) and Long-Short Term Memory (LSTM) for categorization called CNN-LSTM. The experimental outcomes show the high classification accuracy using the deep learning model compared to other machine learning techniques. |
| Databáze: | OpenAIRE |
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