Computer-aided diagnosis of interstitial lung disease: a texture feature extraction and classification approach

1998 ◽  
Author(s):  
Rene Vargas-Voracek ◽  
H. Page McAdams ◽  
Carey E. Floyd, Jr.
Keyword(s):  
Feature Extraction ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Texture Feature ◽  
Computer Aided Diagnosis ◽  
Texture Feature Extraction ◽  
Classification Approach ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis of radiographic patterns of lung disease via MDCT images

2010 ◽  
Vol 5 (3/4) ◽  
pp. 254 ◽  
Author(s):  
Ye Xu ◽  
Edwin J.R. Van Beek ◽  
Kevin R. Flaherty ◽  
Ella A. Kazerooni ◽  
Eric A. Hoffman
Keyword(s):  
Lung Disease ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

2013 ◽  
Vol 18 (7) ◽  
pp. 076001 ◽  
Author(s):  
Ludguier D. Montejo ◽  
Jingfei Jia ◽  
Hyun K. Kim ◽  
Uwe J. Netz ◽  
Sabine Blaschke ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Feature Extraction ◽  
Optical Tomography ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

A Computer Aided System to Discriminate Enhanced Colon Images by Three Data Mining Methods

2013 ◽  
Vol 711 ◽  
pp. 636-640
Author(s):  
Ya Wen Yu ◽  
Hong Mau Lin ◽  
Bor Wen Cheng
Keyword(s):  
Feature Extraction ◽  
Image Data ◽  
Histogram Equalization ◽  
Ensemble Classifier ◽  
Computer Aided Diagnosis ◽  
Ensemble Classification ◽  
Colon Polyps ◽  
Computer Aided ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Computer-aided diagnosis for colon polyps automatically determines the locations of suspicious polyps and masses in Colonoscopy and presents them to doctors, typically as a second opinion. The proposed of Computer-aided diagnosis system consists:Using histogram equalization to do the image in the feature extraction and the classification. The researched image data were collected from a community hospital in Mid-Taiwan. First we used the histogram equalization to do the image enhancement, we got six characteristic values and calculate by the gray-scale co-occurrence matrix to get feature extraction. Finally, we used Decision Tree, Logistic Regression and ENSEMBLE to undergo colonoscopy image data classification. This researched found that difference of six texture parameter between normal and polyp group is significant. The accuracy of ENSEMBLE classification is best (90.00%). It indicates the ENSEMBLE classifier based on texture is effective for classifying polyp from tissue on colon imaging. The results of this study can be help the physician to get reliable and consistent diagnostic results and improve the quality of diagnostic imaging.

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