Mineral image enhancement based on sequential combination of toggle and top-hat based contrast operator

Bai, Xiangzhi

doi:10.1016/j.micron.2012.06.009

Cited by 4 publications

(3 citation statements)

References 30 publications

(37 reference statements)

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“…Algorithms combining the above processes are used to create mechanisms of edge detection for noise removal and background removal as well as for finding specific shapes in images. The transformation of the Morphological top hat is a strong technique for image improvement, particularly in the extraction of bright qualities in the dark background [80][81][82][83][84], With the adaptation of, size of structuring element more than the maximum value of vessel scale results in suppression of non-vascular structures in fundus images. Walter et al [85] have suggested a mathematical morphology supported approach, which recommends contrast improvement and shade correction as preprocessing steps.…”

Section: Morphological Processingmentioning

confidence: 99%

Review of Preprocessing Techniques for Fundus Image Analysis

Joshi¹,

Karule²

2017

AMA_B

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The principal target of preprocessing is to get more appropriate resultant image than its original for further additional analysis. Enhancement of retinal images creates several challenges. The main obstacle is to develop a technique to accommodate the wide variation in contrast inside the image. Necessity of preprocessing methods are for image normalization and to increase the contrast for achieving accurate analysis. This work examined literature in the prior process of digital imaging, in the field of the analysis of fundus image to extract normal and pathologic retinal traits within the context of diabetic retinopathy (DR).

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Section: Morphological Processingmentioning

confidence: 99%

Review of Preprocessing Techniques for Fundus Image Analysis

Joshi¹,

Karule²

2017

AMA_B

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“…Considering that morphological top-hat transformation is a powerful technique for image enhancement, especially in extracting bright features from a dark background [15–19], it is adopted in our method to suppress nonvascular structures by using a structuring element larger than the maximum vessel scale in the medical images.…”

Section: Vessel Enhancementmentioning

confidence: 99%

Vascular Tree Segmentation in Medical Images Using Hessian-Based Multiscale Filtering and Level Set Method

Jin

Yang

Zhang

et al. 2013

Computational and Mathematical Methods in Medicine

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Vascular segmentation plays an important role in medical image analysis. A novel technique for the automatic extraction of vascular trees from 2D medical images is presented, which combines Hessian-based multiscale filtering and a modified level set method. In the proposed algorithm, the morphological top-hat transformation is firstly adopted to attenuate background. Then Hessian-based multiscale filtering is used to enhance vascular structures by combining Hessian matrix with Gaussian convolution to tune the filtering response to the specific scales. Because Gaussian convolution tends to blur vessel boundaries, which makes scale selection inaccurate, an improved level set method is finally proposed to extract vascular structures by introducing an external constrained term related to the standard deviation of Gaussian function into the traditional level set. Our approach was tested on synthetic images with vascular-like structures and 2D slices extracted from real 3D abdomen magnetic resonance angiography (MRA) images along the coronal plane. The segmentation rates for synthetic images are above 95%. The results for MRA images demonstrate that the proposed method can extract most of the vascular structures successfully and accurately in visualization. Therefore, the proposed method is effective for the vascular tree extraction in medical images.

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“…However, dim small target detection in IR images with heavy clutter background is still a challenge. Algorithms based on mathematical morphology, mainly use the Top-Hat [8][9][10][11], Hit-Miss [12], and Toggle [13,14] transform to remove background clutter and detect targets in IR images. By detecting the image background and removing it from the original image, Top-Hat transform identifies the target area.…”

Section: Introductionmentioning

confidence: 99%

Flying small target detection in IR images based on adaptive toggle operator

Marvasti

Mosavi

Nasiri

2018

IET Computer Vision

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Automatic detection and tracking of a small target in infrared (IR) images are of great importance. Toggle operator (TO) is the newest class of non-linear operator morphology that has been widely used in detection and tracking the target in IR images. The most important problem in improving the efficiency of the TO is to use structural elements (SEs) in accordance with signal-to-clutter ratio (SCR) of each image. Generally, the clutters and targets are different in case of each image; therefore, for images with different SCRs, using SEs with fixed pixels and dimensions cannot lead to successful target detection. In this study, a new method is presented based on genetic algorithm to achieve adaptive SE for target detection in IR images. In this method, by designing the SE in accordance with the characteristics of each image, a large amount of background clutter and noise is suppressed and the contrast between target and background is increased. The results of a large set of real IR images including moving targets show that the proposed algorithm is effective in target detection. In the proposed method, the contrast between the target and background clutter is greatly increased while maintaining a low false alarm rate.

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Mineral image enhancement based on sequential combination of toggle and top-hat based contrast operator

Cited by 4 publications

References 30 publications

Review of Preprocessing Techniques for Fundus Image Analysis

Review of Preprocessing Techniques for Fundus Image Analysis

Vascular Tree Segmentation in Medical Images Using Hessian-Based Multiscale Filtering and Level Set Method

Flying small target detection in IR images based on adaptive toggle operator

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