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Lung nodule detection and recognition algorithm based on CT images

Author: SunXuHui
Tutor: TianQiChuan
School: Taiyuan University of Science and Technology
Course: Circuits and Systems
Keywords: Computer-aided diagnosis Pulmonary nodules Fuzzy C-Means Clustering Area marked Support Vector Machine
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 43
Quote: 1
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Abstract


In recent years , lung cancer has become one of the largest malignancy harm to human health , but it is also the lowest cancer survival after diagnosis . But lung cancer if they can in the early discovery and treatment, can greatly enhance the quality of life and survival of patients . Early lung cancer in the form of pulmonary nodules , pulmonary nodules on the image and pulmonary vascular section similar, and both showed class round features . Even if the clinical experience of the doctor is also very prone to misdiagnosis phenomenon . Due to the large amount of data in the CT image characteristics , a patient often hundreds of images , is also very prone to misdiagnosis . Computer-aided diagnosis system based on medical knowledge , combined with computer technology, image processing technology and artificial intelligence , can automatically mark suspicious lesions area doctors improve the work efficiency, reduce the rate of misdiagnosis and missed diagnosis rate . Lung nodule detection algorithm , a new lung nodule detection algorithm , the specific work of the paper is as follows: 1. Thesis globally adaptive threshold method applied to the lungs medical image segmentation by iterative optimization take the optimal threshold to complete the removal of the torso section , and then use the boundary tracking a variety of methods combined to complete the extraction of the pulmonary parenchyma . 2 according to standard fuzzy C -means algorithm computing time is long, the segmentation results are not an ideal situation , the improved algorithm , histogram statistical properties and membership functions to optimize the combination of optimized algorithm . While the completion of the initial parameters of the FCM algorithm . Complete extraction of the region of interest , the simulation results show that the algorithm has good real-time and good segmentation results with improved FCM algorithm . Selected characteristics of lung nodules and proposed based on support vector machine detection algorithm based on the single eigenvalue optimization , optimization of the selected eigenvectors . Normalization methods and the choice of the kernel function to make the corresponding research to improve the accuracy rate criteria , the simulation results show that the algorithm has higher detection rate .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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