Diagnosis of lung nodule using Moran's index and Geary's coefficient in computerized tomography images

被引:16
作者
da Silva, Erick Correa [1 ]
Silva, Aristofanes Correa [1 ]
de Paiva, Anselmo Cardoso [1 ]
Nunes, Rodolfo Acatauassu [2 ]
机构
[1] Univ Fed Maranhao, Fed Univ Maranhao, BR-65085580 Sao Luis, MA, Brazil
[2] Univ Estado Rio De Janeiro, BR-20550900 Rio De Janeiro, RJ, Brazil
关键词
diagnosis of lung nodule; Moran's index; Geary's coefficient; texture analysis;
D O I
10.1007/s10044-007-0081-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper analyzes the application of Moran's index and Geary's coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator's performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.
引用
收藏
页码:89 / 99
页数:11
相关论文
共 51 条
[1]  
[Anonymous], 1975, Discriminant Analysis
[2]  
[Anonymous], 3 D IMAGE PROCESSING
[3]  
Anselin L, 2001, J GEOGRAPHICAL SYSTE, V2, P201
[4]  
Anselin L., 2004, Journal of Geographical Systems, V6, P197, DOI [10.1007/s10109-004-0132-5, DOI 10.1007/S10109-004-0132-5]
[5]   Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening [J].
Arimura, H ;
Katsuragawa, S ;
Suzuki, K ;
Li, F ;
Shiraishi, J ;
Sone, S ;
Doi, K .
ACADEMIC RADIOLOGY, 2004, 11 (06) :617-629
[6]   Computerized detection of pulmonary nodules on CT scans [J].
Armato, SG ;
Giger, ML ;
Moran, CJ ;
Blackburn, JT ;
Doi, K ;
MacMahon, H .
RADIOGRAPHICS, 1999, 19 (05) :1303-1311
[7]   Lung cancer: Performance of automated lung nodule detection applied to cancers missed in a CT screening program [J].
Armato, SG ;
Li, F ;
Giger, ML ;
MacMahon, H ;
Sone, S ;
Doi, K .
RADIOLOGY, 2002, 225 (03) :685-692
[8]   Lung micronodules: Automated method for detection at thin-section CT - Initial experience [J].
Brown, MS ;
Goldin, JG ;
Suh, RD ;
McNitt-Gray, MF ;
Sayre, JW ;
Aberle, DR .
RADIOLOGY, 2003, 226 (01) :256-262
[9]   Computing geostatistical image texture for remotely sensed data classification [J].
Chica-Olmo, M ;
Abarca-Hernández, F .
COMPUTERS & GEOSCIENCES, 2000, 26 (04) :373-383
[10]  
Clark I., 1979, PRACTICAL GEOSTATIST