【摘要】：肺癌是目前已知類型腫瘤中死亡率最高的一種,肺結(jié)節(jié)是早期肺癌的表現(xiàn)形式,肺結(jié)節(jié)檢測(cè)是利用計(jì)算機(jī)輔助肺癌診斷的重要方式。由于肺組織的復(fù)雜,肺結(jié)節(jié)的種類多種多樣,導(dǎo)致了經(jīng)過(guò)圖像預(yù)處理之后仍然存在大量的假結(jié)節(jié)。本文針對(duì)檢測(cè)過(guò)程中,較難區(qū)分真假結(jié)節(jié)的問題,引入群體智能優(yōu)化方法,設(shè)計(jì)并實(shí)現(xiàn)了肺結(jié)節(jié)分類算法,從以下幾個(gè)方面對(duì)肺結(jié)節(jié)分類進(jìn)行了討論： (1)肺結(jié)節(jié)的形態(tài)和紋理多樣,單一特征不能取得較好的描述效果。本文對(duì)肺結(jié)節(jié)進(jìn)行多特征提取,包括灰度特征、紋理特征、梯度特征以及形狀特征,并且將二維與三維特征相結(jié)合,全面描述圖像特性。 (2)針對(duì)肺結(jié)節(jié)數(shù)據(jù)不均衡與特征高維的問題,引入代價(jià)敏感支持向量機(jī)(Cost-sensitive SVM),利用其中的RBF核函數(shù),將多維數(shù)據(jù)映射到高維空間,使原來(lái)在低維空間中不可分的數(shù)據(jù)變得可分,并提出用多分類器組合分類,進(jìn)一步提高分類效果。 (3)將群體智能優(yōu)化方法應(yīng)用于結(jié)節(jié)分類問題中,利用遺傳算法、粒子群算法、人工蜂群算法等方法實(shí)現(xiàn)特征選擇與分類器參數(shù)調(diào)整,有效提高了分類準(zhǔn)確率。 本文設(shè)計(jì)和實(shí)現(xiàn)的真假肺結(jié)節(jié)分類算法,保證了肺結(jié)節(jié)檢測(cè)中對(duì)真假結(jié)節(jié)的有效分類,具有良好的實(shí)用性。
[Abstract]:Lung cancer is the highest mortality among known types of tumors. Pulmonary nodules are the manifestations of early lung cancer. Detection of lung nodules is an important way of computer-aided diagnosis of lung cancer. Because of the complexity of lung tissue and the variety of pulmonary nodules, there are still a lot of false nodules after image preprocessing. In order to solve the problem that it is difficult to distinguish the true and false nodules in the detection process, this paper introduces the swarm intelligence optimization method, and designs and implements the lung nodule classification algorithm. The classification of pulmonary nodules is discussed from the following aspects: (1) the morphology and texture of pulmonary nodules are diverse and the single feature can not get a good description effect. In this paper, multi-feature extraction of pulmonary nodules is carried out, including gray-scale features, texture features, gradient features and shape features, and the two-dimensional and three-dimensional features are combined to describe the image characteristics in an all-round way. (2) to solve the problem of imbalance and high dimension of pulmonary nodule data, cost-sensitive support vector machine (Cost-sensitive SVM),) is introduced to map multi-dimensional data to high-dimensional space by using the RBF kernel function. In order to further improve the classification effect, the data which were not separable in the low dimensional space were made divisible, and the multi-classifier combination was proposed to further improve the classification effect. (3) the swarm intelligence optimization method is applied to the problem of node classification. Genetic algorithm, particle swarm algorithm and artificial bee swarm algorithm are used to realize feature selection and classifier parameter adjustment, which improves the classification accuracy effectively. The algorithm designed and implemented in this paper ensures the effective classification of true and false nodules in the detection of pulmonary nodules and has good practicability.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R563;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 郭薇;魏穎;周翰遜;薛定宇;;基于混合分類的肺結(jié)節(jié)檢測(cè)算法[J];東北大學(xué)學(xué)報(bào)(自然科學(xué)版);2008年11期

2 楊瑞森;肺癌流行病學(xué)和早期診斷新技術(shù)[J];腫瘤防治雜志;2004年07期

3 關(guān)勇;基于支持向量機(jī)的圖像特征選擇研究[J];現(xiàn)代計(jì)算機(jī)(專業(yè)版);2004年04期

4 何中市;梁琰;黃學(xué)全;王健;;肺結(jié)節(jié)檢測(cè)中特征提取方法研究[J];小型微型計(jì)算機(jī)系統(tǒng);2009年10期

，

本文編號(hào)：2437444