Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review

Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy...

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Vydané v:Computers in biology and medicine Ročník 69; s. 97 - 111
Hlavní autori: Sudarshan, Vidya K, Mookiah, Muthu Rama Krishnan, Acharya, U Rajendra, Chandran, Vinod, Molinari, Filippo, Fujita, Hamido, Ng, Kwan Hoong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.02.2016
Elsevier Limited
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ISSN:0010-4825, 1879-0534, 1879-0534
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Abstract Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images. •Application of DWT for cancer detection using ultrasound images is reviewed.•Breast, thyroid, ovary and prostate cancers are considered.•Preprocessing, segmentation and feature extraction using DWT are studied.
AbstractList Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images. •Application of DWT for cancer detection using ultrasound images is reviewed.•Breast, thyroid, ovary and prostate cancers are considered.•Preprocessing, segmentation and feature extraction using DWT are studied.
Abstract Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
Author Molinari, Filippo
Fujita, Hamido
Ng, Kwan Hoong
Acharya, U Rajendra
Chandran, Vinod
Mookiah, Muthu Rama Krishnan
Sudarshan, Vidya K
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  givenname: Muthu Rama Krishnan
  surname: Mookiah
  fullname: Mookiah, Muthu Rama Krishnan
  email: mkm2@np.edu.sg, mrk2k2@gmial.com
  organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
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  givenname: U Rajendra
  surname: Acharya
  fullname: Acharya, U Rajendra
  organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
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  givenname: Vinod
  surname: Chandran
  fullname: Chandran, Vinod
  organization: School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 4000, Australia
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  surname: Fujita
  fullname: Fujita, Hamido
  organization: Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate 020-0693, Japan
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  givenname: Kwan Hoong
  surname: Ng
  fullname: Ng, Kwan Hoong
  organization: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603, Malaysia
BackLink https://www.ncbi.nlm.nih.gov/pubmed/26761591$$D View this record in MEDLINE/PubMed
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1879-0534
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IsPeerReviewed true
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Keywords Thyroid cancer
Wavelet transform
Ultrasound image
Breast cancer
Prostate cancer
Cancer
Ovarian cancer
Language English
License Copyright © 2015 Elsevier Ltd. All rights reserved.
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Snippet Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high...
Abstract Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses...
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SubjectTerms Accuracy
Algorithms
Breast cancer
Cancer
Family medical history
Health risk assessment
Humans
Image Processing, Computer-Assisted - methods
Internal Medicine
Mammography
Medical diagnosis
Medical imaging
Mortality
Mutation
Neoplasms - diagnostic imaging
Other
Ovarian cancer
Prostate cancer
Thyroid cancer
Tomography
Tumors
Ultrasonic imaging
Ultrasonography
Ultrasound image
Urine
Wavelet Analysis
Wavelet transform
Womens health
Title Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review
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