Medical Images Edge Detection Based on Mathematical Morphology
01 Jan 2005-Vol. 2005, pp 6492-6495
TL;DR: A novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise and the experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphologicalEdge detection algorithms.
Abstract: Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms
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TL;DR: The quantitative features analyzed express subvisual characteristics of images which correlate with pathogenesis of diseases, and are broadly classified into four categories: intensity, structure, texture/gradient, and wavelet, based on the types of image attributes they capture.
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TL;DR: A comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.
Abstract: As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.
231 citations
Cites methods from "Medical Images Edge Detection Based..."
...Like the proposed threshold-based segmentation method [13], regionbased image segmentation method [14], and edge detection-based segmentation method [15]....
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TL;DR: Methods adopted for the perinatal brain segmentation methods are reviewed and categorised according to the target population, structures segmented and methodology, and future directions and open challenges for research are discussed.
169 citations
Cites background from "Medical Images Edge Detection Based..."
..., 1997), edge detection (Yu-qian et al., 2005) and clustering techniques (Macqueen, 1967; Bezdek, 1981)....
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01 Jan 2006
TL;DR: In this paper, a comparative study of edge detection algorithms is presented, which proves that Boie-Cox, ShenCastan and Canny operators are better than Laplacian of Gaussian (LOG), while LOG is better than Prewitt and Sobel in case of noisy image.
Abstract: In this paper, classified and comparative study of edge detection algorithms are presented. Experimental results prove that Boie-Cox, ShenCastan and Canny operators are better than Laplacian of Gaussian (LOG), while LOG is better than Prewitt and Sobel in case of noisy image. Subjective and objective methods are used to evaluate the different edge operators. The morphological filter is more important as an initial process in the edge detection for noisy image and used opening-closing operation as preprocessing to filter noise. Also, smooth the image by first closing and then dilation to enhance the image before the edge operators affect.
163 citations
TL;DR: Three modified versions of the conventional moving k-means clustering algorithm are introduced called the fuzzy moving k -means, adaptive moving k'-means and adaptive fuzzyMoving k-Means algorithms for image segmentation application.
Abstract: Image segmentation remains one of the major challenges in image analysis. Many segmentation algorithms have been developed for various applications. Unsatisfactory results have been encountered in some cases, for many existing segmentation algorithms. In this paper, we introduce three modified versions of the conventional moving k-means clustering algorithm called the fuzzy moving k-means, adaptive moving k-means and adaptive fuzzy moving k-means algorithms for image segmentation application. Based on analysis done using standard images (i.e. original bridge and noisy bridge) and hard evidence on microscopic digital image (i.e. segmentation of Sprague Dawley rat sperm), our final segmentation results compare favorably with the results obtained by the conventional k-means, fuzzy c-means and moving k-means algorithms. The qualitative and quantitative analysis done proved that the proposed algorithms are less sensitive with respect to noise. As such, the occurrence of dead centers, center redundancy and trapped center at local minima problems can be avoided. The proposed clustering algorithms are also less sensitive to initialization process of clustering value. The final center values obtained are located within their respective groups of data. This enabled the size and shape of the object in question to be maintained and preserved. Based on the simplicity and capabilities of the proposed algorithms, these algorithms are suitable to be implemented in consumer electronics products such as digital microscope, or digital camera as post processing tool for digital images.
128 citations
References
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Book•
11 Feb 1984
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Abstract: Image Processing and Mathematical Morphology-Frank Y. Shih 2009-03-23 In the development of digital multimedia, the importance and impact of image processing and mathematical morphology are well documented in areas ranging from automated vision detection and inspection to object recognition, image analysis and pattern recognition. Those working in these ever-evolving fields require a solid grasp of basic fundamentals, theory, and related applications—and few books can provide the unique tools for learning contained in this text. Image Processing and Mathematical Morphology: Fundamentals and Applications is a comprehensive, wide-ranging overview of morphological mechanisms and techniques and their relation to image processing. More than merely a tutorial on vital technical information, the book places this knowledge into a theoretical framework. This helps readers analyze key principles and architectures and then use the author’s novel ideas on implementation of advanced algorithms to formulate a practical and detailed plan to develop and foster their own ideas. The book: Presents the history and state-of-the-art techniques related to image morphological processing, with numerous practical examples Gives readers a clear tutorial on complex technology and other tools that rely on their intuition for a clear understanding of the subject Includes an updated bibliography and useful graphs and illustrations Examines several new algorithms in great detail so that readers can adapt them to derive their own solution approaches This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
9,566 citations
"Medical Images Edge Detection Based..." refers methods in this paper
...It was introduced by Matheron [10] as a technique for analyzing geometric structure of metallic and geologic samples....
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...[10] J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, 1982....
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...It provides an alternative approach to image processing based on shape concept stemmed from set theory [10], not on traditional mathematical modeling and analysis....
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...It was extended to image analysis by Serra [10]....
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TL;DR: A system that takes a gray level image as input, locates edges with subpixel accuracy, and links them into lines and notes that the zero-crossings obtained from the full resolution image using a space constant ¿ for the Gaussian, are very similar, but the processing times are very different.
Abstract: We present a system that takes a gray level image as input, locates edges with subpixel accuracy, and links them into lines. Edges are detected by finding zero-crossings in the convolution of the image with Laplacian-of-Gaussian (LoG) masks. The implementation differs markedly from M.I.T.'s as we decompose our masks exactly into a sum of two separable filters instead of the usual approximation by a difference of two Gaussians (DOG). Subpixel accuracy is obtained through the use of the facet model [1]. We also note that the zero-crossings obtained from the full resolution image using a space constant ? for the Gaussian, and those obtained from the 1/n resolution image with 1/n pixel accuracy and a space constant of ?/n for the Gaussian, are very similar, but the processing times are very different. Finally, these edges are grouped into lines using the technique described in [2].
502 citations
"Medical Images Edge Detection Based..." refers methods in this paper
...Conventionally, edge is detected according to some early brought forward algorithms like Sobel algorithm, Prewitt algorithm and Laplacian of Gaussian operator [3], but in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency....
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01 Apr 1987
TL;DR: The blur-minimum morphologic edge operator is defined, its inherent noise sensitivity is less than the dilation or the erosion residue operators, and it is less computationally complex than the facet edge operator.
Abstract: Edge operators based on gray-scale morphologic operations are introduced. These operators can be efficiently implemented in near real time machine vision systems which have special hardware support for gray-scale morphologic operations. The simplest morphologic edge detectors are the dilation residue and erosion residue operators. The underlying motivation for these and some of their combinations are discussed and justified. Finally, the blur-minimum morphologic edge operator is defined. Its inherent noise sensitivity is less than the dilation or the erosion residue operators. Some experimental results are provided to show the validity of these morphologic operators. When compared with the enhancement/thresholding edge detectors and the cubic facet second derivative zero-crossing edge operator, the results show that all the edge operators have similar performance when the noise is small. However, as the noise increases, the second derivative zero-crossing edge operator and the blur-minimum morphologic edge operator have much better performance than the rest of the operators. The advantage of the blur-minimum edge operator is that it is less computationally complex than the facet edge operator.
347 citations
"Medical Images Edge Detection Based..." refers background in this paper
...As the performance of classic edge detectors degrades with noise, morphological edge detector has been studied [11]....
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TL;DR: The paper describes the MIC algorithm in detail, discusses the effects of parametric variations, presents the results of a noise analysis and shows a number of examples of its use, including the removal of scanner noise.
Abstract: Morphological openings and closings are useful for the smoothing of gray-scale images. However, their use for image noise reduction is limited by their tendency to remove important, thin features from an image along with the noise. The paper presents a description and analysis of a new morphological image cleaning algorithm (MIC) that preserves thin features while removing noise. MIC is useful for gray-scale images corrupted by dense, low-amplitude, random, or patterned noise. Such noise is typical of scanned or still-video images. MIC differs from previous morphological noise filters in that it manipulates residual images-the differences between the original image and morphologically smoothed versions. It calculates residuals on a number of different scales via a morphological size distribution. It discards regions in the various residuals that it judges to contain noise. MIC creates a cleaned image by recombining the processed residual images with a smoothed version. The paper describes the MIC algorithm in detail, discusses the effects of parametric variations, presents the results of a noise analysis and shows a number of examples of its use, including the removal of scanner noise. It also demonstrates that MIC significantly improves the JPEG compression of a gray-scale image. >
212 citations
"Medical Images Edge Detection Based..." refers background or methods in this paper
...[7] Richard A P, “A New Algorithm for Image Noise Reduction Using Mathematical morphology,” IEEE Transaction on Image Processing, vol....
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...Mathematical morphology is a new mathematical theory which can be used to process and analyze the images [4-9]....
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TL;DR: The analysis of the multiscale morphological PDEs and of the eikonal PDE solved via weighted distance transforms are viewed as a unified area in nonlinear image processing, which is called differential morphology, and its potential applications to image processing and computer vision are discussed.
Abstract: Image processing via mathematical morphology has traditionally used geometry to intuitively understand morphological signal operators and set or lattice algebra to analyze them in the space domain. We provide a unified view and analytic tools for morphological image processing that is based on ideas from differential calculus and dynamical systems. This includes ideas on using partial differential or difference equations (PDEs) to model distance propagation or nonlinear multiscale processes in images. We briefly review some nonlinear difference equations that implement discrete distance transforms and relate them to numerical solutions of the eikonal equation of optics. We also review some nonlinear PDEs that model the evolution of multiscale morphological operators and use morphological derivatives. Among the new ideas presented, we develop some general 2-D max/min-sum difference equations that model the space dynamics of 2-D morphological systems (including the distance computations) and some nonlinear signal transforms, called slope transforms, that can analyze these systems in a transform domain in ways conceptually similar to the application of Fourier transforms to linear systems. Thus, distance transforms are shown to be bandpass slope filters. We view the analysis of the multiscale morphological PDEs and of the eikonal PDE solved via weighted distance transforms as a unified area in nonlinear image processing, which we call differential morphology, and briefly discuss its potential applications to image processing and computer vision.
134 citations
"Medical Images Edge Detection Based..." refers background in this paper
...Mathematical morphology is a new mathematical theory which can be used to process and analyze the images [4-9]....
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