Name
Abstract | ||
|---|---|---|
In this paper a computational alternative to classify lung nodules using computed tomography (CT) thorax images is presented. The novelty of the method is the elimination of the segmentation stage. The contribution consist of several steps. After image acquisition, eight texture features were extracted from the histogram and the gray level coocurrence matrix (with four different angles) for each CT image. The features were used to train a non-parametric classifier called support vector machine (SVM), used to classify lung tissues into two classes: with lung nodules and without lung nodules. A total of 128 public clinical data set (ELCAP, NBIA) with different number of slices and diagnoses were used to train and evaluate the performance of the methodology presented. After the tests stage, five false negative (FN) and seven false positive (FP) results were obtained. The results obtained were validated by a radiologist to finally obtain a reliability index of 84%. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/MICAI.2013.38 | MICAI (Special Sessions) |
Keywords | Field | DocType |
cancer,computerised tomography,feature extraction,image classification,image texture,matrix algebra,medical image processing,statistical analysis,support vector machines,CT thorax images,computerised tomography,false negative,false positive,feature extraction,gray level coocurrence matrix,histogram,image acquisition,lung nodule classification,nonparametric classifier,reliability index,segmentation stage,support vector machines,texture features,Computed Tomography (CT),Feature extraction,Gray level coocurrence matrix,Lung nodule,Support Vector Machine (SVM) | Histogram,Computer vision,Pattern recognition,Segmentation,Image texture,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Contextual image classification,Classifier (linguistics),Medical diagnosis | Conference |
Citations | PageRank | References |
2 | 0.37 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiram Madero Orozco | 1 | 12 | 1.35 |
| Osslan Osiris Vergara Villegas | 2 | 15 | 6.40 |
| Humberto de Jesús Ochoa Domínguez | 3 | 23 | 4.82 |
| Vianey Guadalupe Cruz Sánchez | 4 | 27 | 7.24 |