Lungs nodule detection framework from computed tomography images using support vector machine.

08:00 EDT 11th April 2019 | BioPortfolio

Summary of "Lungs nodule detection framework from computed tomography images using support vector machine."

The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps-playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well-known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.


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This article was published in the following journal.

Name: Microscopy research and technique
ISSN: 1097-0029


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Medical and Biotech [MESH] Definitions

An imaging technique using a device which combines TOMOGRAPHY, EMISSION-COMPUTED, SINGLE-PHOTON and TOMOGRAPHY, X-RAY COMPUTED in the same session.

A non-invasive method that uses a CT scanner for capturing images of blood vessels and tissues. A CONTRAST MATERIAL is injected, which helps produce detailed images that aid in diagnosing VASCULAR DISEASES.

Computed tomography where there is continuous X-ray exposure to the patient while being transported in a spiral or helical pattern through the beam of irradiation. This provides improved three-dimensional contrast and spatial resolution compared to conventional computed tomography, where data is obtained and computed from individual sequential exposures.

A method of computed tomography that uses radionuclides which emit a single photon of a given energy. The camera is rotated 180 or 360 degrees around the patient to capture images at multiple positions along the arc. The computer is then used to reconstruct the transaxial, sagittal, and coronal images from the 3-dimensional distribution of radionuclides in the organ. The advantages of SPECT are that it can be used to observe biochemical and physiological processes as well as size and volume of the organ. The disadvantage is that, unlike positron-emission tomography where the positron-electron annihilation results in the emission of 2 photons at 180 degrees from each other, SPECT requires physical collimation to line up the photons, which results in the loss of many available photons and hence degrades the image.

Computed tomography modalities which use a cone or pyramid-shaped beam of radiation.

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