Literature DB >> 11515412

Wavelets and imaging informatics: a review of the literature.

J Z Wang1.   

Abstract

Modern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics.

Mesh:

Year:  2001        PMID: 11515412     DOI: 10.1006/jbin.2001.1010

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  5 in total

Review 1.  Radiomics as a Quantitative Imaging Biomarker: Practical Considerations and the Current Standpoint in Neuro-oncologic Studies.

Authors:  Ji Eun Park; Ho Sung Kim
Journal:  Nucl Med Mol Imaging       Date:  2018-02-01

2.  Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI.

Authors:  Ka Young Shim; Sung Won Chung; Jae Hak Jeong; Inpyeong Hwang; Chul-Kee Park; Tae Min Kim; Sung-Hye Park; Jae Kyung Won; Joo Ho Lee; Soon-Tae Lee; Roh-Eul Yoo; Koung Mi Kang; Tae Jin Yun; Ji-Hoon Kim; Chul-Ho Sohn; Kyu Sung Choi; Seung Hong Choi
Journal:  Sci Rep       Date:  2021-05-11       Impact factor: 4.379

3.  Radiomic features and multilayer perceptron network classifier: a robust MRI classification strategy for distinguishing glioblastoma from primary central nervous system lymphoma.

Authors:  Jihye Yun; Ji Eun Park; Hyunna Lee; Sungwon Ham; Namkug Kim; Ho Sung Kim
Journal:  Sci Rep       Date:  2019-04-05       Impact factor: 4.379

4.  Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.

Authors:  Chong Hyun Suh; Kyung Hwa Lee; Young Jun Choi; Sae Rom Chung; Jung Hwan Baek; Jeong Hyun Lee; Jihye Yun; Sungwon Ham; Namkug Kim
Journal:  Sci Rep       Date:  2020-10-16       Impact factor: 4.379

5.  Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis.

Authors:  Jung Su Lee; Jihye Yun; Sungwon Ham; Hyunjung Park; Hyunsu Lee; Jeongseok Kim; Jeong-Sik Byeon; Hwoon-Yong Jung; Namkug Kim; Do Hoon Kim
Journal:  Sci Rep       Date:  2021-02-11       Impact factor: 4.379

  5 in total

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