Image Analysis Using Mathematical Morphology

Haralick, Robert M.; Sternberg, S.; Zhuang, Xinhua

doi:10.1109/tpami.1987.4767941

Cited by 2,315 publications

(993 citation statements)

References 19 publications

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“…Two sequential mathematical morphology operations are performed: dilation and erosion, also known together as morphological closing [18]. A 2D circular structuring element was chosen for these operations, because stereology data is available for every slice and the voxel spacing is larger in the "Z" dimension.…”

Section: Semiautomated Algorithmmentioning

confidence: 99%

Supervised Segmentation of Polycystic Kidneys: a New Application for Stereology Data

et al. 2014

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Stereology is a volume estimation method, typically applied to diagnostic imaging examinations in population studies where planimetry is too time-consuming (Chapman et al. Kidney Int 64:1035-1045, 2003, to obtain quantitative measurements (Nyengaard J Am Soc Nephrol 10: 1100-1123, Michel and Cruz-Orive J Microsc 150:117-136, 1988 of certain structures or organs. However, true segmentation is required in order to perform advanced analysis of the tissues. This paper describes a novel method for segmentation of region(s) of interest using stereology data as prior information. The result is an efficient segmentation method for structures that cannot be easily segmented using other methods.

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Section: Semiautomated Algorithmmentioning

confidence: 99%

Supervised Segmentation of Polycystic Kidneys: a New Application for Stereology Data

et al. 2014

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“…Mathematical morphology uses fuzzy image filtering to extract image components such as boundaries and skeletons; and also to filter the image noise using erosion processes [5], [6]. However, by using morphology in hand detection, the end effect tends to distort the size of the hand.…”

Section: Introductionmentioning

confidence: 99%

Partitioning Method in Noise Reduction to Improve Hand Detection

Woun¹,

Tan²,

Won³

et al. 2013

IJFCC

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Abstract-This paper presents a multi-step algorithm for noise reduction in hand detection by using partitioning method. The main idea of the partitioning method is to divide an image into equal-size blocks, and obtain the percentage of number of pixels with skin color (skin pixels) in each block. Each of the blocks is labelled as either noise or part of the hand based on the percentage of skin pixels in that block. The image is then refined by repeating the processes to work on smaller blocks in the image with different comparison percentage values. The result of this paper shows partitioning method not only can filter the noise, but it also preserves the shape of the hand better, as compared with the Mathematical Morphology algorithm.Index Terms-Hand detection, noise reduction, human computer interaction, partitioning method. I. INTRODUCTIONImage noise is a major issue yet unavoidable in image processing. Noisy images are produced mainly during image sampling or transmission. Various types of noise such as photo electronic noise, impulse and structured noise present challenging problems in image restoration, particularly in object detection. Several noise reduction techniques have been developed to overcome different types of noise and for different aspects of image restoration.Adaptive noise reduction algorithms such as median filtering, K-nearest neighbor averaging, gradient inverse weighted smoothing, etc. have been introduced in [1]-[3]. Median filtering is particularly effective for reducing impulse noise. It is performed by running through the entire image from one region to another, and replaces each pixel in question with the median of its neighboring entries. However the median based filter often cannot differentiate the thin lines and impulse noises and thus mistakenly removes the thin lines [4].Fuzzy image filtering is a nonlinear processing technique used widely as an alternative to linear adaptive filtering. Mathematical morphology uses fuzzy image filtering to extract image components such as boundaries and skeletons; Manuscript received December 19, 2012; revised February 21, 2013 and also to filter the image noise using erosion processes [5], [6]. However, by using morphology in hand detection, the end effect tends to distort the size of the hand.In our research, we propose to use partitioning method in image noise reduction to improve hand detection for an image with background color close to skin color. Our proposed methodology divides an image equally into blocks. The percentage of number of pixels with skin color (skin pixels) in each block is calculated and compared against a threshold value (skin threshold) to decide if the block is noise or part of the hand. A comparison between partitioning method and morphology algorithm is made, particularly on image noise reduction in hand detection. The result produced by partitioning method displays a cleaner hand image with better shape preservation. II. METHODOLOGYOur project uses partitioning method, which adopts divide-and-conquer strategy to re...

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“…Dilation and erosion are building blocks in the design of other morphological tools. Serra ( 1972Serra ( , 1982Serra ( and 1986, Haralick et al ( 1987) and Matheron ( 1965 and1975) are some of the pioneering researchers in binary morphology.…”

Section: Binary Morphologymentioning

confidence: 99%

“…This is the definition given by Haralick et al ( 1987). If the zero in every cube under the top surface in a three dimensional binary image is replaced by a one, then the set consisting of all the ones is called the un1bra.…”

Section: Three Dimensional Binary Imagementioning

confidence: 99%

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Application of mathematical morphology to the analysis of X-ray NDE images

Chackalackal

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Image Analysis Using Mathematical Morphology

Cited by 2,315 publications

References 19 publications

Supervised Segmentation of Polycystic Kidneys: a New Application for Stereology Data

Supervised Segmentation of Polycystic Kidneys: a New Application for Stereology Data

Partitioning Method in Noise Reduction to Improve Hand Detection

Application of mathematical morphology to the analysis of X-ray NDE images

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