Objective

2

Images with complex texture features are difficult to segment. Many methods

such as Gabor filter and structure tensor

have been proposed to segment texture images. However

these methods present a number of drawbacks. For example

methods based on Gabor filter must set multiple scales and directions

and the desirable scale and direction parameters are difficult to select. Methods based on structure tensor must set two fixed directions to analyze intensity variations. However

many texture images include irregular direction features. Moreover

utilizing only the intensity variation information is an inadequate strategy. Morphology similarity and direction uncertainty often exist in nature texture images. Existing methods cannot be utilized to accurately segment nature texture images.

Method

2

Considering the morphology similarity and direction uncertainty of local texture patterns

we proposed the local connection and difference operators to extract the texture features of images. On the one hand

by setting a threshold for each local region

the local connection operation can be used to analyze the intensity distribution and texture morphology features. On the other hand

by considering the intensity variation trait of texture images

the local difference operation can be utilized to extract the local intensity variation. The local connection operation and the local difference operation are complementary. Then

by combining the local similarity and local difference features with the intensity information

the level set energy function was constructed and further minimized to obtain the final segmentation results. The main advantages of the proposed method can be summarized as the following two points. First

the morphology feature was proposed to analyze the local intensity distribution feature of texture images. The intensity distributions of texture images are uncertain for each object region. Thus

extracting the morphology feature of a local region is of great significance. Second

the two proposed operators are complementary. Using only one feature to segment complex texture images is a challenging task. The morphology and intensity difference features can jointly segment images

suggesting that the proposed method can be robust for different texture images.

Result

2

We verified the effectiveness of the two operators by comparing it with other operators

such as Gabor

structure tensor

extended structure tensor

and local similarity factor. Specially

we first analyzed the extraction effects of the morphology feature by comparing the proposed local connection operator with methods based on Gabor filter

on structure tensor and extended structure tensor

local similarity factor. The extracted feature of the local connection operator can be used to efficiently discriminate the object and background regions. Then

the local difference operator was used to compare the proposed approach with traditional texture feature extraction methods. The local different operator can accurately extract the local intensity variation. In addition

we conducted three comparison experiments to confirm whether the proposed method can achieve a better segmentation effect than the methods based on Gabor

structure tensor

extended structure tensor

and local similarity factor. Finally

we tested the robustness of the proposed method for different initial contours and images. The segmentation accuracy of our proposed method exceeded 97%

which was considerably higher than that of other methods. That is

the proposed operator is more effective in discriminating the different texture patterns of images.

Conclusion

2

We proposed two complementary operators

namely

the local connection operator and the local difference operator

to effectively extract texture features. The two extracted features are complementary and can be jointly utilized to segment texture images. Finally

we validated that the proposed method can obtain better segmentation results for natural texture images.