Method for extracting texture based on lung nodules three orthogonality position computed tomography (CT) image and method for forecasting lung cancers

Abstract

The invention discloses a method for extracting texture based on a lung nodules three orthogonality position computed tomography (CT) image, which includes: a building a lung nodules three orthogonality position CT image system; and b extracting lung nodules three orthogonality position texture characteristic parameters through a disperse Curvelet transformation method. The invention further discloses a method for forecasting lung cancers, which includes: a extracting lung CT image texture characteristics; b extracting three orthogonality position CT image and video signs; and c building a forecasting model based on multi-dimensional characteristic parameters including lung nodules three orthogonality position CT image texture characteristics and video signs through a Gradient Boosting algorithm. The method for extracting texture based on lung nodules three orthogonality position CT image and the method for forecasting lung cancer can be used for early diagnosis of pulmonary nodules.

Description

technical field [0001] The invention relates to a medical image diagnosis technology, in particular to a method for extracting a texture of a CT image based on three orthogonal positions of a pulmonary nodule and a method for predicting lung cancer. Background technique [0002] In recent years, lung cancer has always ranked first in cancer mortality in most countries in the world (SR Kim, et al. al.2010)[2], and in the next 30 years, lung cancer will still be the main cause of death in China (JW Wang, et al.2005)[3]. Despite the continuous development of science and technology, the prognosis of lung cancer is still poor, and the 5-year survival rate in most countries is only 10% (G Mountzios, et al. 2010) [4]. However, lung cancer patients can be diagnosed at an early stage, and the 10-year survival rate can reach 92% (N Seki, et al. 2010) [5]. However, more than 80% of the patients are in the middle and advanced stage when they are diagnosed. Therefore, the prevention a...

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Problems solved by technology

[0008] The difference between benign and malignant pulmonary nodules is not only different in the texture of CT images, but also related to many risk factors. However, how to combine the Curvelet texture of pulmonary nodules on three orthogonal CT images of patients with on-site investigations to understand the patient's behavior, environment and other factors, the study of establishing statistical models to explore texture characteristics and risk factors has not been reported

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