A connectionist approach for gray level image segmentation
30 Aug 1992-pp 489-492
TL;DR: A connectionist network is presented for segmenting gray level images and the neural network implementation successfully uses circumstantial evidence and detects multiple winners over the entire range of gray values such that these winners correspond to multiple thresholds for segmented the image.
Abstract: A connectionist network is presented for segmenting gray level images. The network detects the local peaks in the inverted histogram which will correspond to the bottoms of the valleys in the actual histogram. The neural network implementation successfully uses circumstantial evidence and detects multiple winners over the entire range of gray values such that these winners correspond to multiple thresholds for segmenting the image. The dynamics of the network has been analyzed and the conditions for convergence have been established. Experimental results obtained by applying the network for segmenting two X-ray images are presented. >
Citations
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TL;DR: A cork stopper quality classification system using morphological filtering and contour extraction and following (CEF) as the feature extraction method, and a fuzzy-neural network as a classifier is described, which will be used on a daily basis.
Abstract: Cork is a natural material produced in the Mediterranean countries. Cork stoppers are used to seal wine bottles, Cork stopper quality classification is a practical pattern classification problem. The cork stoppers are grouped into eight classes according to the degree of defects on the cork surface. These defects appear in the form of random-shaped holes, cracks, and others. As a result, the classification cork stopper is not a simple object recognition problem. This is because the pattern features are not specifically defined to a particular shape or size. Thus, a complex classification form is involved, Furthermore, there is a need to build a standard quality control system in order to reduce the classification problems in the cork stopper industry. The solution requires factory automation meeting low time and reduced cost requirements. This paper describes a cork stopper quality classification system using morphological filtering and contour extraction and following (CEF) as the feature extraction method, and a fuzzy-neural network as a classifier. This approach will be used on a daily basis. A new adaptive image thresholding method using iterative and localized scheme is also proposed, A fully functioning prototype of the system has been built and successfully tested. The test results showed a 6.7% rejection ratio, It is compared with the 40% counterpart provided by traditional systems. The human experts in the cork stopper industry rated this proposed classification approach as excellent.
58 citations
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TL;DR: In this article, an inverse histogram based pixel mapping step is combined with an edge enhancement step such as unsharp masking to minimize the enhancement of noise components while desired signal components are sharpened.
Abstract: A method and associated device wherein an inverse histogram based pixel mapping step is combined with an edge enhancement step such as unsharp masking. In such an arrangement the inverse histogram based pixel mapping step improves the performance of the unsharp masking step, serving to minimize the enhancement of noise components while desired signal components are sharpened.
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TL;DR: A new method is developed, the spoke transform, to segment the seeds from the background, which uses spoke-like rotating line segments within the two concentric windows to work with spatially varying local backgrounds and to segments the hidden seeds.
Abstract: Permanent implantation of radioactive seeds is a viable and effective therapeutic option widely used today for early stage prostate cancer In order to perform intraoperative dosimetry the seed locations must be determined accurately with high efficiency However, the task of seed segmentation is often hampered by the wide range of signal-to-noise ratios represented in the x-ray images due to highly non-uniform background To circumvent the problem we have developed a new method, the spoke transform, to segment the seeds from the background This method uses spoke-like rotating line segments within the two concentric windows The mean intensity value of the pixels that fall on each rotated line segment best describing the intersection between the seed that we are trying to segment is chosen The inner window gives an indication of the background level immediately surrounding the seeds The outer window is an isolated region not being segmented and represents a non-seed area in need of enhancement and a detection decision The advantages of the method are its ability (1) to work with spatially varying local backgrounds and (2) to segment the hidden seeds Pd-103 and I-125 images demonstrate the effectiveness of the spoke transform
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TL;DR: A new multilevel thresholding method is proposed using sample moment function (SMF) that can make use of the a priori information on whether the target object is found on the darker or lighter part of the scene.
Abstract: A new multilevel thresholding method is proposed using sample moment function (SMF). For the binary case the method can make use of the a priori information on whether the target object is found on the darker or lighter part of the scene. Extensive comparisons using a database of NDT images show that the proposed method outperforms in competition 40 other thresholding methods from the literature M. Sezgin et al. (2001) (2003), based upon both objective scores and subjective evaluation.
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TL;DR: An intelligent system for the identification of micro-calcification clusters in digitised mammograms is proposed that incorporates improved segmentation and better classification and gives a better accuracy.
Abstract: Sophisticated technologies in the field of medical electronics have helped to improve the lives of patients. In this work, an intelligent system for the identification of micro-calcification clusters in digitised mammograms is proposed. The proposed intelligent system consists of two stages: segmentation algorithm and a neural network/kernel system. The system was tested for mammogram images for the Mammographic Image Analysis Society and Digital Database for Screening Mammographic Databases. Different classifiers such as support vector machines, learning vector quantiser, radial basis function and back propagation neural network are used and tested. Support vector machine classifier outperforms other classifiers. The proposed system incorporates improved segmentation and better classification. The proposed intelligent system gives a better accuracy. The execution time is considerably low-befitting real-time applications. The application of the system can be used to identify tumours as benign or malignant.
9 citations
References
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Book•
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TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Abstract: From the Publisher:
This book is a comprehensive introduction to the neural network models currently under intensive study for computational applications. It is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
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01 Feb 1988-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: This paper presents a survey of thresholding techniques and attempts to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures.
Abstract: In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
2,672 citations
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TL;DR: The segmentation algorithm proposed in this paper is a complex form of thresholding which utilizes multiple thresholds and not only works well for simple images but also produces reasonable segmentations for complex images.
Abstract: The segmentation algorithm proposed in this paper is a complex form of thresholdingwhich utilizes multiple thresholds. The algorithm consists of two major components: a threshold selection component and a relaxation component. The threshold selection component is the primary focus of this paper. It automatically selects a threshold so as to maximize the global average contrast of edges detected by the threshold across the image. This algorithm for threshold selection compares favorably with other methods for automatic threshold selection. The threshold selection algorithm can be applied recursively to select additional thresholds by ignoring any edges which have already been detected by previously selected thresholds. The relaxation component utilizes the immediate spatial context of each pixel to update both the label at the pixel and the feature measurement at the pixel. The update function proposes a new feature value at the pixel defined by a weighted average of the central pixel and all of its neighbors. When the local evidence for shifting the feature value is consistent then the value adopted will be close to the proposed value; however, when the local evidence is inconsistent the value adopted will be close to the original value. The relaxation is independently performed for each threshold selected. The resulting binary images are intersected to produce the final segmentation. This algorithm not only works well for simple images but also produces reasonable segmentations for complex images.
266 citations
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01 Sep 1984-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: This method does not rely on the existence of modes on the histogram, and the number of free parameters is reduced, which makes this algorithm essentially automatic and not time consuming.
Abstract: A method for image segmentation and compression based on the intrinsic properties of the distribution function of an image is presented. This method does not rely on the existence of modes on the histogram. The number of free parameters is reduced, which makes this algorithm essentially automatic and not time consuming.
94 citations
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01 Jan 1984-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: A recursive technique for multiple threshold selection on digital images is described, which may be recursively applied first using only those pixels whose intensities are smaller than the threshold and then only those pixel whose intensity values are larger than thereshold.
Abstract: A recursive technique for multiple threshold selection on digital images is described. Pixels are first classified as edge pixels or nonedge pixels. Edge pixels are then classified, on the basis of their neighborhoods, as being relatively dark or relatively light. A histogram of the graytone intensities is obtained for those pixels which are edge pixels and relatively dark and another histogram is obtained for those pixels which are edge pixels and relatively light. A threshold is selected corresponding to the graytone intensity value corresponding to one of the highest peaks from the two histograms. To get multiple thresholds, the procedure may be recursively applied first using only those pixels whose intensities are smaller than the threshold and then only those pixels whose intensities are larger than the threshold.
91 citations
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