Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy—Narrow Band Imaging. Esmaeili, N., Boese, A., Davaris, N., Arens, C., Navab, N., Friebe, M., & Illanes, A. Diagnostics, 11(3):432, March, 2021. ![Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy—Narrow Band Imaging [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download Background: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)—Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. Methods: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). Results: The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. Conclusion: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.
@article{esmaeili_cyclist_2021,
title = {Cyclist {Effort} {Features}: {A} {Novel} {Technique} for {Image} {Texture} {Characterization} {Applied} to {Larynx} {Cancer} {Classification} in {Contact} {Endoscopy}—{Narrow} {Band} {Imaging}},
volume = {11},
copyright = {http://creativecommons.org/licenses/by/3.0/},
shorttitle = {Cyclist {Effort} {Features}},
url = {https://www.mdpi.com/2075-4418/11/3/432},
doi = {10.3390/diagnostics11030432},
abstract = {Background: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)—Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. Methods: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). Results: The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. Conclusion: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.},
language = {en},
number = {3},
urldate = {2021-03-04},
journal = {Diagnostics},
author = {Esmaeili, Nazila and Boese, Axel and Davaris, Nikolaos and Arens, Christoph and Navab, Nassir and Friebe, Michael and Illanes, Alfredo},
month = mar,
year = {2021},
keywords = {classification, contact endoscopy, larynx, narrow band imaging, texture feature extraction},
pages = {432},
}
Downloads: 1
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Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)—Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. Methods: The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). Results: The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. Conclusion: The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.","language":"en","number":"3","urldate":"2021-03-04","journal":"Diagnostics","author":[{"propositions":[],"lastnames":["Esmaeili"],"firstnames":["Nazila"],"suffixes":[]},{"propositions":[],"lastnames":["Boese"],"firstnames":["Axel"],"suffixes":[]},{"propositions":[],"lastnames":["Davaris"],"firstnames":["Nikolaos"],"suffixes":[]},{"propositions":[],"lastnames":["Arens"],"firstnames":["Christoph"],"suffixes":[]},{"propositions":[],"lastnames":["Navab"],"firstnames":["Nassir"],"suffixes":[]},{"propositions":[],"lastnames":["Friebe"],"firstnames":["Michael"],"suffixes":[]},{"propositions":[],"lastnames":["Illanes"],"firstnames":["Alfredo"],"suffixes":[]}],"month":"March","year":"2021","keywords":"classification, contact endoscopy, larynx, narrow band imaging, texture feature extraction","pages":"432","bibtex":"@article{esmaeili_cyclist_2021,\n\ttitle = {Cyclist {Effort} {Features}: {A} {Novel} {Technique} for {Image} {Texture} {Characterization} {Applied} to {Larynx} {Cancer} {Classification} in {Contact} {Endoscopy}—{Narrow} {Band} {Imaging}},\n\tvolume = {11},\n\tcopyright = {http://creativecommons.org/licenses/by/3.0/},\n\tshorttitle = {Cyclist {Effort} {Features}},\n\turl = {https://www.mdpi.com/2075-4418/11/3/432},\n\tdoi = {10.3390/diagnostics11030432},\n\tabstract = {Background: Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. 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