Operations Useful for Similarity-Invariant Pattern Recognition

Doyle, William

doi:10.1145/321119.321123

Cited by 236 publications

(100 citation statements)

References 2 publications

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“…Generally, segmentation is the most pragmatic preprocessing technique that could be applied on manifold filed where the texture of background or foreground should be analyzed (Golchin et al, 2013). As the failure and success rate of every image processing applications is extremely dependent on reliability and scrupulousness of the preprocessing techniques and especially thresholding, therefore, many researchers attempted to introduce thresholding optimization techniques using algorithms such as P-tile (Doyle, 1962), Gray-Level Histogram thresholding "Otsu" (Otsu, 1979), Maximum Entropy with adaptive Genetic algorithm Nie, 2009), Shanbhag algorithm (Shanbhag, 1994), Yen technique (Yen et al, 1995), Entropic thresholding method based on Ant Colony Genetic algorithm (Shen et al, 2009), integration of clustering algorithm and marker controlled watershed segmentation algorithm (Christ et al, 2010) and many other algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Heidari¹,

Abdullah²,

Abdullah³

2015

Journal of Computer Science

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Abstract:In automated pulmonary nodules extraction and lung disease diagnosis by image processing techniques, image segmentation is utilized as a primary and the most essential step of lung tumor analysis. But due to extensive similarity between pulmonary vessels, bronchus and arteries in lung region and the low contrast of the ComputedTomography (CT) image the accuracy of lung tumor diagnosis is highly dependent on the precision of segmentation. Therefore, precise lung CT image segmentation has become a challenging preprocessing task for every lung disease pathological application.In this study, a novel FourDirectional Thresholding (FDT) technique is introduced. This propounded technique segments the pulmonary parenchyma in Computed-Tomography (CT) images using the Similarity-Based Segmentation (SBS). The proposed technique aims to augment the precision of the CT image thresholding by implementing an advanced thresholding approach from four different directions in which the determination of pixels' value as being either on foreground or background is highly dependent on its adjacent pixel's intensity value and the final decision is made based on all four directions' thresholding results. In this study the importance of neighbor pixels in precision of thresholding with FDT technique is demonstrated and the effectiveness of FDT method has been evaluated on different CT images. Eventually the result of segmentation using FDT method is compared by other precursors techniques, which corroborates the high exactitude of proposed technique.

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Section: Related Workmentioning

confidence: 99%

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Heidari¹,

Abdullah²,

Abdullah³

2015

Journal of Computer Science

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“…It is less appropriate in dividing textured images into meaningful regions, but it still has a variety of applications in situations in which the objects and background are homogeneous and smooth. A number of techniques have been proposed for automatic threshold selection; these include global thresholdselection techniques based on gray-level histograms (Doyle, 1962;Prewitt & Mendelson, 1966) or local thresholding methods (Bartz, 1969;Morrin, 1974;Sklansky, 1968;Ullman, 1974;Wolfe, 1969).…”

Section: Resultsmentioning

confidence: 99%

Visual receptive fields and clustering

Micheli-Tzanakou

1983

Behavior Research Methods & Instrumentation

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A pattern recognition technique-clustering-has been used to analyze and evaluate meaningful characteristics of visual receptive fields in the frog tectum. The fields were mapped either by an automatic scanning technique or by a response-feedback method called ALOPEX. The data were then analyzed by the clustering technique, which separates the receptive field into isoresponse regions. The latter can be checked on line by stimulating the eye with each cluster and with combinations of clusters. In this way, existing nonlinearities can be checked objectively.

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“…Over the past five decades, many automatic threshold selection methods have been reported in literature. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] In late 80's, Sahoo et al 16 published a survey of optimum thresholding methods while Lee et al 17 reported results of a comparative study of several thresholding methods. Glasbey 18 published results of another comparative study involving eleven histogram-based thresholding algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…A relatively recent survey of thresholding algorithms for change detection in a surveillance environment has been presented by Rosin and Ioannidis. 36 Among early works on automatic thresholding, Prewitt and Mendelson 19 suggested using valleys in a histogram, while Doyle 20 advocated the choice of median. Otsu 21 developed a thresholding method maximizing between-class variance.…”

Section: Introductionmentioning

confidence: 99%

A new multi-object image thresholding method based on correlation between object class uncertainty and intensity gradient

2012

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Purpose: Image thresholding and gradient analysis have remained popular image preprocessing tools for several decades due to the simplicity and straight-forwardness of their definitions. Also, optimum selection of threshold and gradient strength values are hidden steps in many advanced medical imaging algorithms. A reliable method for threshold optimization may be a crucial step toward automation of several medical image based applications. Most automatic thresholding and gradient selection methods reported in literature primarily focus on image histograms ignoring a significant amount of information embedded in the spatial distribution of intensity values forming visible features in an image. Here, we present a new method that simultaneously optimizes both threshold and gradient values for different object interfaces in an image that is based on unification of information from both the histogram and spatial image features; also, the method works for unknown number of object regions. Methods: A new energy function is formulated by combining the object class uncertainty measure, a histogram-based feature, of each pixel with its image gradient measure, a spatial contextual feature in an image. The energy function is designed to measure the overall compliance of the theoretical premise that, in a probabilistic sense, image intensities with high class uncertainty are associated with high image gradients. Finally, it is expressed as a function of threshold and gradient parameters and optimum combinations of these parameters are sought by locating pits and valleys on the energy surface. A major strength of the algorithm lies in the fact that it does not require the number of object regions in an image to be predefined. Results: The method has been applied on several medical image datasets and it has successfully determined both threshold and gradient parameters for different object interfaces even when some of the thresholds are almost impossible to locate in the histogram. Both accuracy and reproducibility of the method have been examined on several medical image datasets including repeat scan 3D multidetector computed tomography (CT) images of cadaveric ankles specimens. Also, the new method has been qualitatively and quantitatively compared with Otsu's method along with three other algorithms based on minimum error thresholding, maximum segmented image information and minimization of homogeneity-and uncertainty-based energy and the results have demonstrated superiority of the new method. Conclusions: We have developed a new automatic threshold and gradient strength selection algorithm by combining class uncertainty and spatial image gradient features. The performance of the method has been examined in terms of accuracy and reproducibility and the results found are better as compared to several popular automatic threshold selection methods.

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Operations Useful for Similarity-Invariant Pattern Recognition

Cited by 236 publications

References 2 publications

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Visual receptive fields and clustering

A new multi-object image thresholding method based on correlation between object class uncertainty and intensity gradient

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