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

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• 作者：Hiram Madero Orozco (1)
  Osslan Osiris Vergara Villegas (1)
  Vianey Guadalupe Cruz S谩nchez (2)
  Humberto de Jes煤s Ochoa Dom铆nguez (2)
  Manuel de Jes煤s Nandayapa Alfaro (1)
  
• 关键词：CADx system ; Lung nodules ; CT scan ; Wavelet feature descriptor ; Gray level co ; ocurrence matrix ; Support vector machine ; Texture
• 刊名：BioMedical Engineering OnLine
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：14
• 期：1
• 全文大小：1,640 KB
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• 作者单位：Hiram Madero Orozco (1)
  Osslan Osiris Vergara Villegas (1)
  Vianey Guadalupe Cruz S谩nchez (2)
  Humberto de Jes煤s Ochoa Dom铆nguez (2)
  Manuel de Jes煤s Nandayapa Alfaro (1)
  
  1. Departamento de Ingenier铆a Industrial y Manufactura, Instituto de Ingenier铆a y Tecnolog铆a, Universidad Aut贸noma de Ciudad Ju谩rez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Ju谩rez, Chihuahua, M茅xico
  2. Departamento de Ingenier铆a El茅ctrica y Computaci贸n, Instituto de Ingenier铆a y Tecnolog铆a, Universidad Aut贸noma de Ciudad Ju谩rez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Ju谩rez, Chihuahua, M茅xico
  
• 刊物类别：Engineering
• 出版者：BioMed Central
• ISSN：1475-925X

文摘

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.