Two-dimensional ARMA modeling for breast cancer detection and classification

Bouaynaya, Nidhal; Zieliński, Jerzy; Schonfeld, Dan

doi:10.1109/spcom.2010.5560514

Cited by 20 publications

(8 citation statements)

References 9 publications

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“…This provides an ARMA(4, 3) which has been shown a good model for ultrasound signal [3,4]. Without loss of generality, we constrain the first polynomial coefficients to take a unit value, so that a…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Continuous-domain ARMA modeling for ultrasound tissue characterization

Maggio

Alessandrini

Speciale

et al. 2011

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

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In this study, we investigate the possibility of applying a continuous-time ARMA (CARMA) model to radio-frequency ultrasound signals. We consider the effect of the discretization process on the parameters of the continuous system, and we take into account the exponential nature of the autocorrelation function of the model to derive continuous-domain information from the parameters of the discrete ARMA process. We validate the effectiveness of the CARMA model parameters for the characterization of ultrasound tissues on a sequence of phantom images that represent various concentrations of scatterers. We also compare the proposed CARMA coefficients and the traditional ARMA parameters on the basis of their performance in discriminating between phantom tissues. We show that working in the continuous domain brings additional useful information to characterize the imaged materials.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Among them, shift-variant autoregressive moving-average (ARMA) processes were found suitable to model the ultrasound signals and images and to make the acquisition more robust in its estimation of the uncorrupted tissue response [1,2]. More recently, 2D ARMA modeling was proposed to improve computer-aided detection of breast tumors [3,4].…”

Section: Introductionmentioning

confidence: 99%

Continuous-domain ARMA modeling for ultrasound tissue characterization

Maggio

Alessandrini

Speciale

et al. 2011

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

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“…The results of this minimization are summarized in the following equation: (24) where is the sum of the covariance matrices of forward and backward vertical auxiliary PEF vectors. The constant is the th row vector of an identity matrix with an appropriate order.…”

Section: A Quarter-plane Modelsmentioning

confidence: 99%

“…Other potential applications are also emerging in areas such as high-resolution ISAR radar imaging [16], [21], [22] and breast cancer detection and classification [23], [24].…”

Section: Introductionmentioning

confidence: 99%

New Efficient 2-D Lattice Structures for General Autoregressive Modeling of Random Fields

Kayran

Camcioglu

2014

IEEE Trans. Signal Process.

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In this paper, we present a new efficient general 2-D autoregressive (AR) lattice modeling technique of random fields. Our proposed approach is capable of simultaneously providing all possible types of 2-D causal quarter-plane (QP) and asymmetric half-plane (ASHP) AR models for an arbitrary rectangular shape of the prediction support region (PSR). It is shown that it is also possible to obtain various noncausal half-plane AR models without any additional computational cost. This new lattice structure introduces only one row or one column of new observation points into the existing rectangular PSR when the order of the model increases in the horizontal or vertical direction at each of the recursive order incrementation stage, respectively. Starting with a given random data field, a set of auxiliary forward and backward prediction error fields (PEFs) for the horizontal and vertical directions are generated. After recursive order updates to the desired size of the rectangular PSR, all types of causal QP/ASHP and noncausal half-plane 2-D lattice models are obtained by deriving the appropriate PEF from the remaining ones. In addition to developing the basic theory, the presentation includes the derivation of the synthesis model transfer functions from the calculated lattice parameters. It is shown that the proposed auxiliary horizontal and vertical backward prediction error filters inherently generates the mutually orthogonal realization vectors and thus are optimum for all stages. The theory has been confirmed by computer simulations.Index Terms-2-D signal processing, 2-D lattice filters, linear prediction, causal and noncausal modeling, 2-D spectral estimation.

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“…Following the methodology proposed by Jergy Zielinski, Nidhal Bouynaya [5] in which breast image is represented as a 2D random field {x [n, m], (n, m) ∈ Z 2 }. We define a total order on the discrete lattice as follows -(i, j) ≤− (s, t) ⇐⇒−i ≤−s and j ≤−t.…”

Section: A Representation Of Arima Modelmentioning

confidence: 99%

ARIMA model based breast cancer detection and classification through image processing

Kumar

Kumari

Ranjan

et al. 2014

2014 Students Conference on Engineering and Systems

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Computer Aided Diagnosis (CAD) has changed the way of medical diagnostics. As similar to other walk of diagnostics field, CAD is having high potential in breast cancer prognosis because of its highest accuracy. CAD may play a very important role in developing countries i.e. EIT-MEM (Electrical Impedance Tomography -Multi-frequency Electrical Impedance Mammography) device being used for breast cancer defection. MEM-EIT produces tomography based mammograms which are considered most reliable method of early detection of breast cancer. Cancer diagnostic expert all over the world find this noninvasive technique very accurate as it is one dimensional representation of images in terms of temperature however the accuracy is limited and investigator fail to take into account the spatial co-ordination between the pixels which is crucial in cancerous tumour detection and their classification (cancerous or normal) in EIT (Electrical Impedance Tomography) -based mammogram images. In this study, we are trying to focus an algorithms based CAD (Computer Aided Diagnosis) model for tumour detection and classification. We model it by ARIMA model (autoregressive integrated moving average (ARIMA) model) and parameter estimation will be performed using leassquare method. Our system classifies the tumour into three categories-(i) healthy tissue (ii) benign tissue (iii) cancerous tissue along with above three segments the performance analysis between 2D image and 1D image will be done for better accuracy and sensitivity detection. Index Terms--ARIMA Model, Breast Cancer Detection, EIT MEM

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Two-dimensional ARMA modeling for breast cancer detection and classification

Cited by 20 publications

References 9 publications

Continuous-domain ARMA modeling for ultrasound tissue characterization

Continuous-domain ARMA modeling for ultrasound tissue characterization

New Efficient 2-D Lattice Structures for General Autoregressive Modeling of Random Fields

ARIMA model based breast cancer detection and classification through image processing

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