Three Dimensional Reconstruction Of Human Lung Cancer Tissue In Electrical Tomography

The electrical tomography technology due to the advantages of economic and real-time response has becoming an advanced industrial detection technology in process inspection field. In the biomedical field, traditional medicine detection technology used in CT imaging has disadvantages such as high cost and high radiation, so it is not suitable for patients with long-term use. The electrical tomography technology to overcome the above problem has shown great potential and broad application prospects. As the basis of biomedical detection, this paper studies the use of electronic medical biological characteristics of tomographic imaging techniques.The specific research contents and mainly contributions are shown as follows:(1) The extraction of lung tissue electrical impedance dataIn the past three years, we went to the general hospital of Tianjin to gather human cancer tissue and normal tissue and test the impedance data under 31 frequency of 109 cases. Among them 91 were valid data. The database contains four typical calsses of lung cancer: squamous carcinoma, adenocarcinoma, large cell carcinoma and small cell carcinoma. This provides the experimental base for the classification of the electrical characteristics of the lung cancer tissue.(2) The identification of cancer tissueCole–Cole circle radius and the modulus electrical impedance value are two features of the classification and identification of cancerous tissue and normal tissue. And we using ROC curve to evaluate the identification accuracy. Through the analysis of the experimental data, the recognition rate can reach more than 78%. Besides, we analysis the influence of patients' age, sex, smoking history, history of chemotherapy on the detection accuracy. The influence of these results for the electrical tomography technique is applied to the early detection and treatment.(3) The three-dimensional reconstruction of cancerous tissue we using c++ combined with VKT to reconstruction existing 2-D CT image sequence into three-dimensional. 3-D simulation of the cancer tumor provides the advantages those 2-D slices incomparable. Our method is different from the existing 3-D reconstruction technology. Our method is based on the effectiveness indicator to determine the optimal class number of two-dimensional CT image, achieve the optimal partition. Results of experiment show that the proposed approach has higher spatial resolution than the existing method, so that we can observe the organization form of the tumor more intuitively.