Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data

Soni, Tarun Kumar; Zeidler, J.R.; Ku, W.H.

doi:10.1109/83.236534

Cited by 159 publications

(89 citation statements)

References 24 publications

(9 reference statements)

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“…Concerning image segmentation and specification of regions of interest (ROIs), several methods have been proposed such as classical image filtering and local thresholding [9,12,39,45], techniques based on mathematical morphology [13,60], stochastic fractal models [25,26], wavelet analysis [3,7,22,23,46,52,56,57] and multiscale analysis based on a specialized Gaussian and Peitgen [32]. Furthermore, various classification methodologies have been reported for the characterization of ROI such as, rule-based systems [9,12], fuzzy logic systems [11], statistical methods based on Markov random fields [20] and support vector machines [3].…”

Section: Introductionmentioning

confidence: 99%

An automatic microcalcification detection system based on a hybrid neural network classifier

Papadopoulos

Fotiadis

Likas

2002

Artificial Intelligence in Medicine

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A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms. The proposed method is based on a three-step procedure: (a) preprocessing and segmentation, (b) regions of interest (ROI) specification, and (c) feature extraction and classification. The reduction of false positive cases is performed using an intelligent system containing two subsystems: a rule-based and a neural network sub-system. In the first step of the classification schema 22 features are automatically computed which refer either to individual microcalcifications or to groups of them. Further reduction in the number of features is achieved through principal component analysis (PCA). The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (A z ). In particular, the A z value for the Nijmegen dataset is 0.91 and for the MIAS is 0.92. The detection specificity of the two sets is 1.80 and 1.15 false positive clusters per image, at the sensitivity level higher than 0.90, respectively. #

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Section: Introductionmentioning

confidence: 99%

An automatic microcalcification detection system based on a hybrid neural network classifier

Papadopoulos

Fotiadis

Likas

2002

Artificial Intelligence in Medicine

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“…This method processes an image pixel by pixel using a I-D scanning scheme, and consequently, it considers the correlation of pixels in only one direction. Soni et al [8] used a 2-D adaptive LMS filter to detect and isolate small objects with broad-band spectra from background clutter with a narrow-band spectrum. More recently, several other authors [9]- [11] have developed different 2-D LMS-based adaptive algorithms.…”

Section: Two-dimensional Block Diagonal Lms Adaptive Filteringmentioning

confidence: 99%

Two-dimensional block diagonal LMS adaptive filtering

Azimi-Sadjadi

Pan

1994

IEEE Trans. Signal Process.

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The conventional LMS scheme was first extended to the 2-D case by Hadhoud and Thomas [7]. Their 2-D LMS algorithm is particularly useful in various image processing areas such as image enhancement and image data compression where the variations in local statistics of the image must be taken into account. This method processes an image pixel by pixel using a I-D scanning scheme, and consequently, it considers the correlation of pixels in only one direction. Soni et al. [8] used a 2-D adaptive LMS filter to detect and isolate small objects with broad-band spectra from background clutter with a narrow-band spectrum. More recently, several other authors [9]-[11] have developed different 2-D LMS-based adaptive algorithms. In [10], a new 2-D sequential adaptive filtering scheme was proposed which uses variable step-size to improve the convergence behavior. This algorithm was then extended in [11] for block processing using a scalar adaptation rule.This paper is concerned with the development of a 2-D block diagonal LMS (BDLMS) algorithm based on the application of the 2-D block processing method [12]. A 2-D diagonal scanning is employed to preserve the local correlational information of pixels in both directions. The convergence behavior of the 2-D BDLMS filter is studied. To consider the nonstationarity inherent in real-world images, a variable step size rule is given. Applications of the 2-D BDLMS in image estimation, filtering, and detection areas are studied. Simulation results for filtering additive noise from a corrupted image are presented, and a comparison is made between the proposed 2-D BDLMS and the standard WienerAbstract-This paper is concerned with the development of a two-dimensional (2-D) adaptive filters using the block diagonal least mean squared (BDLMS) method. In this adaptive filtering scheme the image is scanned and processed block by block in a di~gonal fashion, and the filter weights are adjusted o?ce per block rather than once per pixel. The diagonal scanmng is adopted to avoid the problems inherent in the 1-]) standard scanning schemes and to account for the correlations in tw.o directions. The weight updating equation for 2-D BDLMS IS derived and the convergence properties of the algorithms are investig~ted. Simulation results that indicate the etTectiv~nes~of the 2-D BDLMS when used for image enhancement, estimation, and detection applications are presented.

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“…In past decades, many techniques have been proposed for suppressing the clutter and enhancing the detect-ability of small targets. Soni et al have evaluated the performance of the two-dimensional least mean square (TDLMS) adaptive filter in target detection [4]. Sang et al have enhanced the target detection method of two-dimensional normalized LMS (TDNLMS) filter [5].…”

Section: Introductionmentioning

confidence: 99%

Small target detection using the Bilateral Filter based on Target Similarity Index

Bae

Lee

Sohng

2010

IEICE Electron. Express

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Abstract:This paper presents a new bilateral filter (BF) using the adaptive standard deviation based on the target similarity index (TSI) for small target detection. At first, the threshold value of pixel's TSI in window decides whether any pixel is a potential small target or not. The TSIs of the potential small target pixels are mapped to standard deviations of domain and range filters of the BF by linear mapping and the two standard deviations increase in proportion to the TSI for blurring the small targets. For the more blurred small targets, the filter size according to the TSI is increased. Experimental results demonstrate that the proposed method is more robust and efficient than the conventional methods.

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Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data

Cited by 159 publications

References 24 publications

An automatic microcalcification detection system based on a hybrid neural network classifier

An automatic microcalcification detection system based on a hybrid neural network classifier

Two-dimensional block diagonal LMS adaptive filtering

Small target detection using the Bilateral Filter based on Target Similarity Index

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