Abstract
The present research paper deals with the reconstruction process of thoracic spine images through 2D thoracic spine x-ray images with the help of Artificial Neural Network and Fuzzy Set Rules. The present work has been carried out in two phases: the Modelling phase and Understanding phase. In the modeling phase, a knowledge-based model has been framed with natural human anatomy spine images collected from different sources. The formation of model has been done after proper selection and extraction of geometric features from natural human anatomy images. The features are based on shape size orientation. In the present paper the main focus has been kept on the extraction of twenty features based on the different orientation of thoracic spine image. A unique innovative approach for feature selection, extraction and mapping are adopted in the present paper for understanding the model, for proper reconstruction of 3D thoracic spine images through thoracic spine X-ray/MRI/CT images. The mapping and classification process has been done using Support Vector Machine. The experimental work has been carried out using Artificial Neural Network and Fuzzy Set Rules.
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Agrawal, S., Sisodia, D.S., Nagwani, N.K. (2019). Neuro-Fuzzy Approach for Reconstruction of 3-D Spine Model Using 2-D Spine Images and Human Anatomy. In: Prateek, M., Sharma, D., Tiwari, R., Sharma, R., Kumar, K., Kumar, N. (eds) Next Generation Computing Technologies on Computational Intelligence. NGCT 2018. Communications in Computer and Information Science, vol 922. Springer, Singapore. https://doi.org/10.1007/978-981-15-1718-1_9
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