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The Study of Detection Arithmetic for Pulmonary Opacity Based on CT Image

Author: LiDaoJing
Tutor: FanLiNanï¼›SunShenShen
School: Shenyang University
Course: Control Theory and Control Engineering
Keywords: Pulmonary nodules Top-hat filter Gabor filters Support Vector Machine Computer-aided diagnosis
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 38
Quote: 0
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Abstract


Lung cancer is a common internal malignancy, is so far the greatest threat to human life and health of one of the top five causes of death. Lung cancer at an early stage, when patients have no obvious symptoms, are often ignored. Patients because of cough, hemoptysis, chest pain and other clinical symptoms visit, usually collected CT images, the majority of tumors found on the chest X-ray is already in late. If an earlier detection of lung cancer, can be to treat the patient early, thereby increasing the chance of cure. Early lung cancer is usually in the form of pulmonary nodules, pulmonary nodules can be detected early diagnosis and treatment of lung cancer. For computer-aided diagnosis of pulmonary nodules on the subject, the research is mainly to do the following aspects: (1) lung region segmentation. The lung segmentation purpose is to facilitate the next step of the detection of lung nodules, and therefore can not be any destruction or alteration of the lung parenchyma. For this purpose, this study used the region growing method, binarization thresholds lung region segmentation, and achieved satisfactory segmentation results. (2) the region of interest from the extraction. This study proposes a more accurate hybrid algorithm to extract the region of interest. Extracting the region of interest, often will extract a large amount of non-pulmonary nodule region, affect the subsequent classification results. Pin this problem, this study proposes a method to detect lung nodules Top-hat and Gabor filter hybrid algorithm, to reduce false positives to ensure complete extraction of the pulmonary region of interest based on the number of terms of a more good results. (3) of the region of interest for feature extraction and classification. There are a large number of false positives due to the extracted region of interest in the lungs, and so in order to improve the detection accuracy of the results, the effective characteristics of the region of interest after extraction, the SVM classifier is applied to the nodular region and normal tissue classification. This study by comparing selected characteristics of specific select the the apparent specificity eight characteristics, including the four characteristics of shape, two gray feature, and two position characteristics. And for the region of interest is extracted characteristics of small sample, the use of SVM classifier to classify the regions of interest based on the extracted features, and get a better classification results. Clinical image simulation, good test results, including sensitivity to 0.9436, undetected rate of 0.0563. The proposed algorithm is compared in terms of can better meet the needs of doctors standard.

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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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