Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system

Keshani, Mohsen; Azimifar, Zohreh; Tajeripour, Farshad; Boostani, Reza

doi:10.1016/j.compbiomed.2012.12.004

Cited by 169 publications

(77 citation statements)

References 21 publications

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“…4 is the visual comparative result of the method presented in this paper along with a presented method by Ye [3]. This has been extracted from [9].…”

Section: B Experiments Results Based On Level Setmentioning

confidence: 99%

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A level set based method for lung segmentation in CT images

Azimi

Rabbani

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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In this paper an automatic computer-aided (CAD) method is utilized for lung segmentation using computed tomography (CT) images. We segmented lung regions -based on the CT data-with nodules attached to the chest wall by using level set modeling. This method is made up of 3 steps: In the first step, an adaptive fuzzy thresholding operation is used to binarize the CT images; in the second step, the lung with non-isolated nodules is segmented applying both level set modeling and convex hull algorithm. In the third step, by using the shape features of lung lobe, the lung is segmented. The experimental results show an accuracy of 98% by our method with out performance other exiting methods.

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“…4 is the visual comparative result of the method presented in this paper along with a presented method by Ye [3]. This has been extracted from [9].…”

Section: B Experiments Results Based On Level Setmentioning

confidence: 99%

“…In the next stage, the lung binary image is produced by an adaptive fuzzy thresholding method [9]. Most of the lung space pixels have lesser illumination intensity than the walls.…”

Section: A Image Pre-processingmentioning

confidence: 99%

A level set based method for lung segmentation in CT images

Azimi

Rabbani

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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“…Accuracy of this system is 88% by R. Boostani [1]. Region of interest extracted using ACM technique by M. Keghani [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of lung image and nodule detection using fuzzy inference system

Roy

Sirohi

Patle

2015

International Conference on Computing, Communication &Amp; Automation

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The objective of this paper is to classify likely cancerous and noncancerous lung image and to detect the location of the nodule in the lung image provided by CT scan. The novelness of this paper is to provide better accuracy and assists radiologist to analyze CT scan images of lung accurately. This efficient proposed method consists of image enhancement, extracting region of interest using Active Contour Model, extracting spatial features from segmented image, train those feature vectors and classify the test image through Fuzzy Inference System. This proposed method performance is compared with one of the most efficient and popular existing method Support Vector Machine and shows better accuracy of 94.12% Keywords-Active ContourModel;Fuzzy Inference System;Support Vector Machine;image enhancement or preprocessing I.

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“…Traditionally, SVM classifier uses a defaults set of C and γ in solving the pattern classification problems [15].…”

Section: Nodule Classificationmentioning

confidence: 99%

Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

Sentana

Jawas

Asri

2018

J. Phys.: Conf. Ser.

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Abstract. Lung nodule is an early indicator of some lung diseases, including lung cancer. In Computed Tomography (CT) based image, nodule is known as a shape that appears brighter than lung surrounding. This research aim to develop an application that automatically detect lung nodule in CT images. There are some steps in algorithm such as image acquisition and conversion, image binarization, lung segmentation, blob detection, and classification. Data acquisition is a step to taking image slice by slice from the original *.dicom format and then each image slices is converted into *.tif image format. Binarization that tailoring Otsu algorithm, than separated the background and foreground part of each image slices. After removing the background part, the next step is to segment part of the lung only so the nodule can localized easier. Once again Otsu algorithm is use to detect nodule blob in localized lung area. The final step is tailoring Support Vector Machine (SVM) to classify the nodule. The application has succeed detecting near round nodule with a certain threshold of size. Those detecting result shows drawback in part of thresholding size and shape of nodule that need to enhance in the next part of the research. The algorithm also cannot detect nodule that attached to wall and Lung Chanel, since it depend the searching only on colour differences.

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Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system

Cited by 169 publications

References 21 publications

A level set based method for lung segmentation in CT images

A level set based method for lung segmentation in CT images

Classification of lung image and nodule detection using fuzzy inference system

Automatic Solitary Lung Nodule Detection in Computed Tomography Images Slices

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