CIE Lab color space based gray threshold segmentation method

A technology of color space and grayscale threshold, which is applied in the field of image processing, can solve problems such as color image segmentation, achieve effective correction schemes and reduce difficulty

Active Publication Date: 2015-05-06
INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

It has no sensitive parameters and no complex iterative process, but uses the brightness and chroma information of the object and background to convert the segmentation of color images into grayscale image segmentation, thus solving a part of color image segmentation. The problem

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  • CIE Lab color space based gray threshold segmentation method
  • CIE Lab color space based gray threshold segmentation method
  • CIE Lab color space based gray threshold segmentation method

Examples

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Embodiment example 1

[0071] For verifying that the present invention provides the effect of the grayscale threshold value segmentation scheme based on CIE Lab color space, carried out following experiment: select experimental sample as attached Figure 5 shown. See attached image 3 RGB color images and their respective components, and attached Figure 4 In the Lab color image and their respective components, it can be seen that the original R, G, and B components cannot distinguish the foreground and background well in the grayscale image, and the L channel has a similar effect, which shows that the brightness cannot be effectively used Distinguish between foreground and background. The a channel distinguishes the foreground and the background very well in terms of grayscale. The respective grayscale separations of the L, a, and b channels are 0.0989, 0.6871, and 0.4173, which can also reflect the effectiveness of the separation. The results are attached Figure 5 As shown in , the superimpos...

Embodiment example 2

[0073] This method can also be well applied to medical image segmentation, with Figure 6 A pseudocolor RGB medical image of a brain tumor is shown, along with the corresponding Lab color and grayscale images, Figure 7 The corresponding L, a, b grayscale images are shown. It can be seen from the comparison that the contrast between the tumor and the surrounding tissue in the a and b channels is stronger, and in fact the separation is also the largest in the b channel, so that compared with the grayscale image, the threshold method can be better used for pre-segmentation in order to more accurately Extract the diseased tissue.

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Abstract

A CIE Lab color space based gray threshold segmentation method comprises the following steps of 1 transforming an image of a RGB color space into a CIE Lab color space, 2 conducting Gaussian histogram filtering on all gray channels of the CIE Lab color space, 3 adopting an Otsu threshold value method to calculate threshold values of the gray channels and adjusting the threshold values as local minimum, 4 calculating gray separation degrees of the gray channels of the CIE Lab color space, selecting the gray channel with highest gray separation degree and adopting the corresponding threshold value calculated in the step 3 to perform binaryzation division. By means of the CIE Lab color space based gray threshold segmentation method, the problem of division of a part of color images is well solved. In addition, the division operation can serve as pre-division operation conducted on complicated color images, and proposed separation degree index can also serve as a standard for image estimation.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a simple and effective global threshold segmentation method based on conversion from color space to gray space. Background technique [0002] Image segmentation is a classic problem in image processing, and it is also the primary problem in image analysis and pattern recognition. Image segmentation refers to distinguishing different regions with special meaning in the image, these regions do not cross each other, and each region satisfies the consistency of a specific region. [0003] Most of the current research work is focused on the segmentation of grayscale images, and the mainstream image segmentation methods can be divided into three categories. First, the pixel-based method, also known as the threshold-based scheme, the more classic schemes include the peak-to-valley method, the minimum error method, the maximum inter-class variance method, and the maximum entrop...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T2207/10024
Inventor 龙鹏鲁华祥边昳徐露露王俭陈旭龚国良金敏陈刚
Owner INST OF SEMICONDUCTORS - CHINESE ACAD OF SCI
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