NDLI: Lung Nodule Classification in CT Thorax Images Using Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Madero Orozco, H. Vergara Villegas, O.O. De Jesus Ochoa Dominguez, H. Cruz Sanchez, V.G.
Copyright Year	2013
Description	Author affiliation: Inst. de Ing. y Tecnol., Univ. Autonoma de Ciudad Juarez, Ciudad Juárez, Mexico (Madero Orozco, H.; Vergara Villegas, O.O.; De Jesus Ochoa Dominguez, H.; Cruz Sanchez, V.G.)
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%.
Starting Page	277
Ending Page	283
File Size	540233
Page Count	7
File Format	PDF
ISBN	9781479926046
e-ISBN	9781479926053
DOI	10.1109/MICAI.2013.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Gray level coocurrence matrix Lungs Computed tomography Computed Tomography (CT) Support Vector Machine (SVM) Lung nodule Feature extraction Reliability Equations Cancer
Content Type	Text
Resource Type	Article

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