NDLI: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Content Provider	Springer Nature : BioMed Central
Author	Madero Orozco, Hiram Vergara Villegas, Osslan Osiris Cruz Sánchez, Vianey Guadalupe Ochoa Domínguez, Humberto de Jesús Nandayapa Alfaro, Manuel de Jesús
Abstract	Background Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. Methods The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. Results The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. Conclusions The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.
Related Links	https://biomedical-engineering-online.biomedcentral.com/counter/pdf/10.1186/s12938-015-0003-y.pdf
Ending Page	20
Page Count	20
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s12938-015-0003-y
Journal	BioMedical Engineering OnLine
Issue Number	1
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2015-02-12
Access Restriction	Open
Subject Keyword	Biomedical Engineering and Bioengineering Biomaterials Biotechnology Biomedical Engineering CADx system Lung nodules CT scan Wavelet feature descriptor Gray level co-ocurrence matrix Support vector machine Texture Biomedical Engineering/Biotechnology
Content Type	Text
Resource Type	Article
Subject	Biomaterials Radiological and Ultrasound Technology Biomedical Engineering Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.5/2023

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