Research On Fast Segmentation Method For Image With Intensity Inhomogeneous

Image segmentation is an important basis for image tracking and image understanding.Research fields such as aerospace,medicine,biology,geology,and materials science rely on correct image segmentation.In the real world,due to illumination,imaging equipment,etc.,the distribution of image intensity is inhomogeneous.The intensity inhomogeneity causes the edges of the image to be blurred,which may cause inaccurate segmentation results.In the past decade,the problem of intensity inhomogeneity has been a research hotspot of image segmentation.Many novel methods have been proposed.However,when processing images with severe intensity inhomogeneity,most of the segmentation results of these methods may be inaccurate.A few methods such as the LATE level set model have strong resistance to intensity inhomogeneity,but the computational cost of these models is generally higher.In response to these problems,this paper proposes three algorithm strategies to improve the efficiency and quality of segmentation results for images with intensity inhomogeneity.1.A rectangular narrow band method is proposed.The traditional narrow-band method constrains the calculation area to the strip area near the active contour,thereby reducing the calculation.However,there are still many redundant calculation areas near the active contour.This paper proposes an activity constraint strategy,which constrains the narrow band to the real active region,further narrows the band area and reduces the complexity of calculation.The rectangular narrowband method is combined with the LATE model to improve the segmentation efficiency and ensure the segmentation quality.Experiments have shown that the ratio of the area of the narrowband of the rectangle to the area of the traditional narrowband is gradually reduced to zero,as the number of iterations increases.It shows that the rectangular narrow band method effectively reduces the amount of redundancy calculation.For the 10 images whose intensity inhomogeneity is increasing gradually,the rectangular narrow-band method is compared with the direct narrow-band method,the DTM narrowband method,and the original LATE method without narrowband.Experiments show that the direct narrowband method and the DTM narrowband method are slower than the original LATE method.For the images with severe intensity inhomogeneity,the segmentation quality of the direct narrow-band method and the DTM narrow-band method is greatly affected,and the rectangular narrow-band method can improve the segmentation efficiency of the LATE model while maintaining a good segmentation effect.2.A fast image segmentation method based on LOG response zero crossing is proposed.This method treats the contours of the LOG response as a collection of sub-contours and counts the average gradient of each sub-contour.Then set a reasonable threshold,and eliminate the sub-contour whose average gradient is below the threshold,and obtain the target sub-contour set,i.e.the final segmentation result.This method ensures the formation of a closed segmentation area.The average gradient can reflect the intensity of the region well,which makes the method have a good segmentation effect on images with severe intensity inhomogeneity.The experimental results show that the segmentation efficiency of the fast image segmentation method based on LOG response zero-crossing is better than that of RSF,LIC,LSACM,LATE,etc.,and the segmentation effect is equivalent to the LATE method.In addition,the method does not require initialization and is highly robust.3.A level set method based on diffusion equation is proposed.We regard the image domain as a slice of a metal material.The image information produces a symmetrical heat source and a cold source at the edge of the image,and the heat is freely diffused inside the slice under the action of the diffusion term.At the beginning of the evolution,the heat source and the cold source exchange heat with the slice,resulting in uneven temperature distribution of the slice.In the middle stage of evolution,the heat source term gradually approaches zero.Under the action of the diffusion term,the heat in the local hot zone gradually spreads to the local colder region.Eventually,the temperature of the target area is raised to a higher level,and the temperature of the background area is lowered to a lower level,thereby achieving separation of the target area from the background area.Experiments show that our method based on thermal diffusion level set can quickly locate the edge of the image and has little dependence on the initial contour.In order to evaluate the segmentation results,the JSC values of our method were compared with the other seven level set models.Experiments show that the JSC values of our model are comparable to those of the LATE model designed for images with severe intensity inhomogeneity,and are superior to other models.The segmentation efficiency of this method is significantly better than that of LATE,LSACM,RSF,DRLSE and so on.In short,this paper studies the segmentation of gray-scale inhomogeneous images from two aspects of quality and efficiency,and proposes three segmentation methods.Among them,the rectangular narrow band method further optimizes the segmentation efficiency based on the LATE model.The fast image segmentation method based on LOG response zero-crossing and the diffusion-based equation method are two new segmentation models,which can ensure good segmentation effect and greatly improved the segmentation efficiency.