Feature extraction from cancer images using local phase congruency: A reliable source of image descriptors

Szilágyi, Tünde; Brady, Michael

doi:10.1109/isbi.2009.5193281

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…The phase-based model for feature extraction proposes that the useful features of a signal are observed at locations with meaningful Fourier components. This is validated by the human visual system where we perceive image features at points where the phase values of its Fourier components are maximally in congruence [1].…”

Section: Introductionmentioning

confidence: 75%

Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition

Shojaeilangari

Yau

et al. 2012

2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Although facial expression plays an important role in human interaction, automated facial expression analysis is still a challenging task. This paper presents a novel facial descriptor based on Phase Congruency (PC) and Local Binary Pattern (LBP) for facial expression recognition. The proposed descriptor, named Binary Pattern of Phase Congruency (BPPC), is an oriented and multi-scale local descriptor that is able to encode various patterns of face images. It is constructed by applying LBP on the oriented PC images. We evaluated the proposed method using the Cohn-Kanade (CK + ) database. In our experiment, we achieved an overall detection rate of 93.83% for the six basic emotions. This shows the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 75%

Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition

Shojaeilangari

Yau

et al. 2012

2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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“…This means that a universal threshold value can be set to denote a boundary, applicable to many image types. In 2009 a development of Kovesi's phase congruency algorithm was reported by Szilagyi and Brady [27], and used to emphasise already relatively clear boundaries in microscale tumour vessel and macroscale pre-clinical pancreatic cancer ultrasound images. However, at blurred or diffuse edges Kovesi's phase congruency is diminished, the significance of which is unclear.…”

Section: Future Directionsmentioning

confidence: 99%

Automated delineation of radiotherapy volumes: are we going in the right direction?

Whitfield¹,

Price²,

Price³

et al. 2013

BJR

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ABSTRACT. Rapid and accurate delineation of target volumes and multiple organs at risk, within the enduring International Commission on Radiation Units and Measurement framework, is now hugely important in radiotherapy, owing to the rapid proliferation of intensity-modulated radiotherapy and the advent of four-dimensional image-guided adaption. Nevertheless, delineation is still generally clinically performed with little if any machine assistance, even though it is both time-consuming and prone to interobserver variation. Currently available segmentation tools include those based on image greyscale interrogation, statistical shape modelling and body atlas-based methods. However, all too often these are not able to match the accuracy of the expert clinician, which remains the universally acknowledged gold standard. In this article we suggest that current methods are fundamentally limited by their lack of ability to incorporate essential human clinical decision-making into the underlying models. Hybrid techniques that utilise prior knowledge, make sophisticated use of greyscale information and allow clinical expertise to be integrated are needed. This may require a change in focus from automated segmentation to machine-assisted delineation. Similarly, new metrics of image quality reflecting fitness for purpose would be extremely valuable. We conclude that methods need to be developed to take account of the clinician's expertise and honed visual processing capabilities as much as the underlying, clinically meaningful information content of the image data being interrogated. We illustrate our observations and suggestions through our own experiences with two software tools developed as part of research council-funded projects.

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“…The monogenic parameters can be further exploited to drive processing techniques which self adapt to the local features. For instance, a multiscale monogenic signal has been exploited to identify feature points in various 2D medical images [10]. We propose here an adaptive anisotropic smoothing technique which consists in steering the Riesz-wavelet coefficients according to the monogenic directions Uv, thresholding them, and reconstructing a smoothed image by inverting the Riesz-wavelet pyramid.…”

Section: Microscopy Image Analysis and Processingmentioning

confidence: 99%

3D steerable wavelets and monogenic analysis for bioimaging

Chenouard

Unser

2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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In this paper we introduce a 3D wavelet frame that has the key property of steerability. The proposed wavelet frame relies on the combination of a 3D isotropic wavelet transform with the 3D Riesz operator which brings steerability to the pyramid. The novel transform enjoys self reversibility and exact steering of the basis functions in any 3D direction by linear combination of the primary coefficients. We exploit the link between the Riesz transform and the directional Hilbert transform to define a multiresolution monogenic signal analysis in 3D which achieves multiscale AM/FM signal decomposition. We give an example of application of the 3D monogenic wavelet frame in biological imaging with the enhancement of anisotropic structures in 3D fluorescence microscopy.

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Feature extraction from cancer images using local phase congruency: A reliable source of image descriptors

Cited by 7 publications

References 12 publications

Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition

Feature extraction through Binary Pattern of Phase Congruency for facial expression recognition

Automated delineation of radiotherapy volumes: are we going in the right direction?

3D steerable wavelets and monogenic analysis for bioimaging

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