Echographic differentiation of histological types of intraocular melanoma

Thijssen, J.M.; Verbeek, A. M.; Romijn, R.; Wolff-Rouendaal, D. De; Oosterhuis, J. A.

doi:10.1016/0301-5629(91)90120-l

Cited by 28 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results confirm forecasted [28] advantages of the RF-based quantitative analysis, allowing additional digital manipulation for overcoming certain limitations of qualitative interpretation. The second issue of our approach was application of complex algorithms for tissue characterization in relation with backscattering spectra model-based methods [18, 29, 30] and empirical or statistical estimation methods [25]. The backscattering models were theoretically and practically tested [29] with regard to the properties of the observed backscattering spectra.…”

Section: Discussionmentioning

confidence: 99%

Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

Jurkonis

Janušauskas

Marozas

et al. 2012

The Scientific World Journal

View full text Add to dashboard Cite

Algorithms and software were developed for analysis of B-scan ultrasonic signals acquired from commercial diagnostic ultrasound system. The algorithms process raw ultrasonic signals in backscattered spectrum domain, which is obtained using two time-frequency methods: short-time Fourier and Hilbert-Huang transformations. The signals from selected regions of eye tissues are characterized by parameters: B-scan envelope amplitude, approximated spectral slope, approximated spectral intercept, mean instantaneous frequency, mean instantaneous bandwidth, and parameters of Nakagami distribution characterizing Hilbert-Huang transformation output. The backscattered ultrasound signal parameters characterizing intraocular and orbit tissues were processed by decision tree data mining algorithm. The pilot trial proved that applied methods are able to correctly classify signals from corpus vitreum blood, extraocular muscle, and orbit tissues. In 26 cases of ocular tissues classification, one error occurred, when tissues were classified into classes of corpus vitreum blood, extraocular muscle, and orbit tissue. In this pilot classification parameters of spectral intercept and Nakagami parameter for instantaneous frequencies distribution of the 1st intrinsic mode function were found specific for corpus vitreum blood, orbit and extraocular muscle tissues. We conclude that ultrasound data should be further collected in clinical database to establish background for decision support system for ocular tissue noninvasive differentiation.

show abstract

Section: Discussionmentioning

confidence: 99%

Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

Jurkonis

Janušauskas

Marozas

et al. 2012

The Scientific World Journal

View full text Add to dashboard Cite

show abstract

“…The radiofrequency data acquisition and processing systems have been described extensively elsewhere (Romijn et al , 1990Thijssen et al 1991). …”

Section: Methodsmentioning

confidence: 99%

Echographic differentation of intraocular melanomas by computer analysis

Thijssen

Verbeek

Romijn

et al. 1992

Acta Ophthalmologica

Self Cite

View full text Add to dashboard Cite

The aim of this study was to assess the d a erentiation of histologically different types of choroidal melanomas by means of clinical and quantitative acoustic/texture parameters of echograms. Clinical parameters were graded by morphological, kinetic and quantitative statements about the A-and B-mode echograms by a skilled diagnostician. Acoustic/texture parameters were obtained by processing and analysing radio frequency, AM-demodulated and FM-demodulated echograms. The latter data were preprocessed to remove influence induced by beam diffraction and focusing. The best set of four clinical echographic parameters enabled a retrospective classification of spindle type melanomas vs. mixed + epithelioid type melanomas with an accuracy of 77% (area under the ROC-curve of 86%). The discriminant analysis performed with the best four acoustic/texture parameters (n = 30) yielded a sensitivity of 89%, a specificity of 92%, and an area under the ROC-curve which corresponds to a probability of correct classification of 96.6%. When using these data prospectively to classify tumours of unknown cell type (n = 21), this classification could be performed in 86% of cases.

show abstract

“…However, a large body of literature exists for texture analysis of ultrasound, MRI, CT, fluorescence microscopy, light microscopy, and other digital images. For example, in the eye, texture analysis of ultrasound images has been used to differentiate histological types of intraocular melanoma [16]. In another study, posterior capsule opacification was assessed from digital camera images using the texture analysis techniques of variance measurement and co-occurrence [17].…”

Section: Texture Analysismentioning

confidence: 99%

Texture analysis for tissue classification of optical coherence tomography images

Gossage

Tkaczyk

Rodriquez

et al. 2003

Advanced Biomedical and Clinical Diagnostic Systems

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is a cross-sectional imaging modality capable of acquiring images to depths of a few millimeters at resolutions ranging from 10-15 µm. This makes OCT useful for visualizing layers and structures within the tissue, but not effective for seeing in vivo cellular level detail. Random spatially dependent speckle patterns were seen in our images due to the coherent properties of light utilized in OCT. These speckle patterns are dependent on various optical parameters of the system, including numerical aperture, as well as the size and distribution of light scattering particles within the sample.The purpose of this study is to evaluate the application of statistical and spectral texture analysis techniques for differentiating tissue types based on the structural and speckle content in OCT images. Good correct classification rates were obtained when five different bovine tissues were compared in pairs, averaging 80% correct, and reasonable rates were obtained comparing normal vs. abnormal mouse lung tissue, averaging 64.0% and 88.6%, respectively.This study has shown that texture analysis of OCT images may be capable of differentiating tissue types without reliance on visible structures.

show abstract

Echographic differentiation of histological types of intraocular melanoma

Cited by 28 publications

References 22 publications

Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

Algorithms and Results of Eye Tissues Differentiation Based on RF Ultrasound

Echographic differentation of intraocular melanomas by computer analysis

Texture analysis for tissue classification of optical coherence tomography images

Contact Info

Product

Resources

About