Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features

Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features

The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze th...

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Bibliographic Details
Published in:Journal of imaging Vol. 6; no. 2; p. 6
Main Authors: Khehrah, Noor, Farid, Muhammad Shahid, Bilal, Saira, Khan, Muhammad Hassan
Format: Journal Article
Language:English
Published: Basel MDPI AG 24-02-2020
MDPI
Subjects:
Algorithms
Blood vessels
Computed tomography
computed tomography (ct)
computer-aided detection (cad)
Consortia
Digital imaging
Feature extraction
Histograms
Image detection
Lung cancer
Lungs
Medical diagnosis
Medical imaging
Medical prognosis
Morphology
nodule detection
Nodules
Support vector machines
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Summary:The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.
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ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging6020006