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

Autor: Vianey Guadalupe Cruz Sánchez, Hiram Madero Orozco, Osslan Osiris Vergara Villegas, Humberto de Jesús Ochoa Domínguez, Manuel de Jesús Nandayapa Alfaro
Jazyk: angličtina
Předmět:
CT scan
CADx system
Lung Neoplasms
Support Vector Machine
Gray level co-ocurrence matrix
Wavelet Analysis
Biomedical Engineering
Sensitivity and Specificity
Pattern Recognition
Automated
Biomaterials
Automation
Imaging
Three-Dimensional
Wavelet
Region of interest
Wavelet feature descriptor
medicine
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Computer vision
Texture
Lung cancer
Referral and Consultation
Incidental Findings
Radiological and Ultrasound Technology
business.industry
Research
Solitary Pulmonary Nodule
Wavelet transform
Nodule (medicine)
Pattern recognition
General Medicine
medicine.disease
Data set
Lung nodules
ROC Curve
Area Under Curve
Radiographic Image Interpretation
Computer-Assisted
Tomography
Artificial intelligence
medicine.symptom
Tomography
X-Ray Computed
business
Algorithms
Zdroj: BioMedical Engineering
ISSN: 1475-925X
DOI: 10.1186/s12938-015-0003-y
Popis: 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.
Databáze: OpenAIRE
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