Morphological contrast measure and contrast enhancement: One application to the segmentation of brain MRI

Mendiola-Santibáñez, Jorge D.; Terol-Villalobos, Iván R.; Herrera-Ruíz, Gilberto; Fernández-Bouzas, Antonio

doi:10.1016/j.sigpro.2007.02.008

Cited by 25 publications

(13 citation statements)

References 27 publications

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“…It is important to mention not only that the age of the six participants in this study is seven years old; but also that, the GM and WM are segmented by using the methodology followed in (20). From the latter procedure, only some output images are presented to illustrate such segmentations.…”

Section: Ct Computed Tomographymentioning

confidence: 99%

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Comparison of Granulometric Studies of Brain Slices from Normal and Dissociated Strabismus Subjects Through Morphological Transformations

Mendiola-Santibáñez¹,

Gallegos-Duarte²,

Gómez-Meléndez³

et al. 2012

Advances in Brain Imaging

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Section: Ct Computed Tomographymentioning

confidence: 99%

“…As a consequence, it is necessary to separate WM and GM to obtain the granulometric patterns of these regions. In this work, GM and WM were segmented by applying the methodology used in (20). Some output images illustrating the segmentation of such regions are presented in Fig.…”

Section: Granulometric Patternsmentioning

confidence: 99%

Comparison of Granulometric Studies of Brain Slices from Normal and Dissociated Strabismus Subjects Through Morphological Transformations

Mendiola-Santibáñez¹,

Gallegos-Duarte²,

Gómez-Meléndez³

et al. 2012

Advances in Brain Imaging

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“…assign this value to the corresponding pixel in output image and the opening transformation. For dark regions, the black top-hat is used, this transformation is determined by the subtraction between the closing transformation and the original image 22 . The key mechanism of the Top-hat transform under the opening operator is the local comparison of a shape, the structural element, with the object that will be transformed.…”

Section: Segmentation: Extracting the Region Of Interestmentioning

confidence: 99%

“…Meanwhile the method has found immense applications in several other fields, including medical diagnostics, histology, industrial inspection, computer vision, and character recognition 8,22 . Mathematical morphology examines the geometrical structure of an image by probing it with small patterns, called structuring elements, of varying size and shape, just the way a blind man explores the world with his fingers or a stick.…”

Section: Introductionmentioning

confidence: 99%

Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

Hassanien

Abraham

2008

Int. J. Comp. Intel. Appl.

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The objective of this research is to illustrate how rough sets can be successfully integrated with mathematical morphology and provide a more effective hybrid approach to resolve medical imaging problems. Hybridization of rough sets and mathematical morphology techniques has been applied to depict their ability to improve the classification of breast cancer images into two outcomes: malignant or benign cancer. Algorithms based on mathematical morphology are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, features are extracted characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. The rough set approach to attribute reduction and rule generation is further presented. Finally, rough morphology is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. To evaluate performance of the presented rough morphology approach, we tested different mammogram images. The experimental results illustrate that the overall performance in locating optimal orientation offered by the proposed approach is high compared with other hybrid systems such as rough-neural and rough-fuzzy systems.

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“…If s 1 = s2, r1 = 0, r2 = L-l, the transformed image only two gray levels, the greatest contrast at this time, but the complete loss of image details. [9,10] …”

mentioning

confidence: 99%

The research of X-ray bone fracture image enhancement algorithms

Tie-Rui

Zheng

2010

2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering

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As a lower level of the image processing, image enhancement is the preparatory stage of the image processing. Its target is to improve the visual effects and purposeful emphasis the whole or part characteristic of the graphics in the designated applications to enlarge the difference between the different objects in the graphics. So it can meet the requirement of some special analysis. This paper introduced some representative algorithms of image enhancement: contrast enhancement, histogram equalization, smoothing filter, median filter, the ideal low pass filter, butter worth low filter, the ideal high pass filter, butter worth high filter and homomorphic filter. At last, the enhanced image by use of different methods were threshold segmentation and edge extraction, the results were compared to identify suitable methods for X-ray bone image enhancement algorithms.Keywords-image enhancement;histogram equalization X-ray; bone fracture image; homomorphic filter I. I NTRODUCTIONImage in the collection process will inevitably be under the influence of various factors such as: sensor sensitivity, noise and ADC quantization, which led to the image can not achieve satisfactory results, so in order to better research on image, made on the original image to improve the behavior of highly targeted, called image enhancement. The algorithm can be divided into spatial and frequency domain algorithm. Processing algorithm based on spatial gray level directly to do computing. The algorithm is based on frequency domain image of a transform domain transformation on the image for a correction coefficient, is an indirectly enhanced algorithms. As the purpose of image enhancement are highly subjective, and enhance the results of the merits of the standard mostly determined with the subjective observer, there is no universal quantitative standard, so in the practical application of image enhancement in the different specific applications, there is a variety of enhancement algorithms to choose from. For this reason, only through several of the image enhancement algorithm in-depth, systematic research, compare, find out the advantages and disadvantages of the corresponding application occasions, this is the subject of the purpose and significance of the study. II. THE IMAGE ENHANCE OF SPACE DOMAINA. Contrast enhancementContrast enhancement, also known as contrast stretching to enhance image contrast is enhanced between the various parts of the original image contrast, the basic 978-1-4244-7956-611 0/$26.00 ©20 1 0 IEEE idea is to improve the image processing gray level dynamic range, that is, the original image by increasing the intensity of a two dynamic range between the value achieved. Figure 1 is a typical change in contrast enhancement, can be seen from the figure 1, the original image's gray value from 0 to s 1 and s2 to the L-l reduces the dynamic range between, and in between s 1 and s2 increased dynamic range, so that the contrast within the reach of the enhancement effect, in which the s 1, s2, r1, r2 can be achieved with the di...

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Morphological contrast measure and contrast enhancement: One application to the segmentation of brain MRI

Cited by 25 publications

References 27 publications

Comparison of Granulometric Studies of Brain Slices from Normal and Dissociated Strabismus Subjects Through Morphological Transformations

Comparison of Granulometric Studies of Brain Slices from Normal and Dissociated Strabismus Subjects Through Morphological Transformations

Rough Morphology Hybrid Approach for Mammography Image Classification and Prediction

The research of X-ray bone fracture image enhancement algorithms

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