Adaptive morphological operators, fast algorithms and their applications

Cheng, Wood-Hi; Venetsanopoulos,

doi:10.1109/icsyse.1991.161126

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…It is still one of the state-of-the-art linear methods for edge preserving smoothing. Various nonlinear edge preserving morphological smoothing algorithms are also available in the literature [16][17][18][19]. Lerallut et al [16] define adaptive morphological operators, called amoeba filter, where shape of the structuring element varies from pixel to pixel, determined by thresholding the geodesic distance D i j, lm of the pixel (i + l, j + m) from the center pixel (i, j) [ Fig.…”

Section: Regularizer and Smoothing Kernelmentioning

confidence: 99%

Adaptive Morphologic Regularizations for Inverse Problems

Purkait

Chanda

2013

Lecture Notes in Computer Science

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Abstract. Regularization is an well-known technique for obtaining stable solution of ill-posed inverse problems. In this paper we establish a key relationship among the regularization methods with edge-preserving noise filtering method which leads to an efficient adaptive regularization methods. We show experimentally the efficiency and superiority of the proposed regularization methods for some inverse problems, e.g. deblurring and super-resolution (SR) image reconstruction.

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Section: Regularizer and Smoothing Kernelmentioning

confidence: 99%

Adaptive Morphologic Regularizations for Inverse Problems

Purkait

Chanda

2013

Lecture Notes in Computer Science

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“…The new operators also attain some distinct properties that exploit the geometric structures [4]. The intuitive geometric operations are the most distinguishing characteristics of the NOP and the NCR They differ from most of the existing linear and nonlinear image processing techniques which base their operations on the local statistics [4].…”

Section: Properties Of the Nop And The Ncpmentioning

confidence: 99%

“…Unlike many conventional approaches whose output depends on the shape of their operational window, the output of the NOP and NCP depends on the structure of the processed images. This property can be considered as a geometric optimization [4]. Here, it is assumed that noise and non-noise patterns are only different by their sizes.…”

Section: Properties Of the Nop And The Ncpmentioning

confidence: 99%

“…Therefore, reducing the number of comparisons required per pixel will result in less computational effort. close examination will reveal that those pixels included in the final structuring element before locating the minimum pixel for the first time, will share the same output fMIN as (i,j) [4]. Denote the included pixels as zl,..., zk,..., z u, where k corresponds to the order in which zk is found in the search.…”

Section: Computational Complexity and Fast Algorithmsmentioning

confidence: 99%

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Adaptive morphological filters for color image enhancement

Deng-Wong

Cheng²,

Venetsanopoulos

1996

Journal of Intelligent and Robotic Systems

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In this paper, we investigate the performance of an adaptive morphological filter on color image processing, and we develop fast algorithms for the operation. The adaptive filter is based on a new type of opening (NOP) and closing operators (NCP). The operational window of the NOP and NCP can alter its shape according to the local geometry of the processed images. Our experimental results show the effect of the geometric-based adaptivity in preserving the edges, details, highlights, smoothness and color appearance, while removing noise from color images.

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“…Since these procedures are generally based on neighborhood operations, and examine each pixel during the evolution of the edge, the outcomes are usually highly accurate. On the other hand, although there are several optimized algorithms in the literature [10,11], they are generally computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

3D Segmentation of Medical Images Using a Fast Multistage Hybrid Algorithm

Peters

2006

Int J CARS

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In this paper, we propose a fast multistage hybrid algorithm for 3D segmentation of medical images. We first employ a morphological recursive erosion operation to reduce the connectivity between the object to be segmented and its neighborhood; then the fast marching method is used to greatly accelerate the initial propagation of a surface front from the user defined seed structure to a surface close to the desired boundary; a morphological reconstruction method then operates on this surface to achieve an initial segmentation result; and finally morphological recursive dilation is employed to recover any structure lost in the first stage of the algorithm. This approach is tested on 60 CT or MRI images of the brain, heart and urinary system, to demonstrate the robustness of this technique across a variety of imaging modalities and organ systems. The algorithm is also validated against datasets for which "truth" is known. These measurements revealed that the algorithm achieved a mean "similarity index" of 0.966 across the three organ systems. The execution time for this algorithm, when run on a 550 MHz Dual PIII-based PC runningWindows NT, and extracting the cortex from brain MRIs, the cardiac surface from dynamic CT, and the kidneys from 3D CT, was 38, 46 and 23 s, respectively.

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Adaptive morphological operators, fast algorithms and their applications

Cited by 8 publications

References 6 publications

Adaptive Morphologic Regularizations for Inverse Problems

Adaptive Morphologic Regularizations for Inverse Problems

Adaptive morphological filters for color image enhancement

3D Segmentation of Medical Images Using a Fast Multistage Hybrid Algorithm

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