Research On Multi-threshold Segmentation Algorithm Of Medical Image Based On Histogram

Medical image segmentation technology is an indispensable part of medical image processing,the quality of segmentation will directly affect the next registration,fusion and other further operations,thereby affecting the accuracy of treatment.A good quality segmentation image can largely help doctors diagnose and improve the efficiency of the whole treatment process.In the image segmentation algorithm,threshold segmentation algorithm has been widely studied and applied because of its simplicity,efficiency and easy understanding.When single threshold cannot satisfy the requirement of further segmentation,it can expand to multiple thresholds.However,when single threshold is extended to multi-threshold,there are some disadvantages,such as high time complexity,low accuracy,and so on,which affects the practical application.In this paper,through the use of histogram information,put forward to improve the above shortcomings of the multi-threshold segmentation algorithm.The exhaustive method used by traditional multi-threshold segmentation to obtain the threshold.The method can get more accurate results,but it is extremely time-consuming.With each threshold added,the time complexity increases exponentially.In view of the shortcoming of high time complexity in multi-threshold segmentation algorithm,this paper creatively proposes the idea of histogram region merging.The 256 gray levels of the image are regarded as the original thresholds,and the area between two thresholds are regarded as a region.The merging criteria in each area are calculated.After calculation,one threshold is reduced in each iteration,which is to select adjacent regions for merging.Until the threshold number meets the requirements.The idea of histogram region merging reduces the time complexity of the algorithm to O(L)level,and fully realizes the requirement of real-time.In order to improve the accuracy of merging,the algorithm uses the probability as the quantity information and the variance as the change degree of the amount of information in the histogram to calculate.Finally,experiments show that the algorithm reduces the time complexity while ensuring the accuracy of segmentation.Otsu algorithm is one of the most widely used algorithms in image thresholding segmentation.However,the Otsu algorithm is vulnerable to noise,edge and other information,resulting in inaccurate segmentation,which is more obvious when extended to multi-threshold.In this paper,multi-threshold Otsu algorithm based on D-value and gray histogram dimensionality reduction is proposed.Firstly,the histogram is extended to two dimensions,and gray and D-value are used as the information of two-dimensional histogram.The D-value can clearly reflect the relationship between the pixels to be processed and other points in the window.By the size of the D-value,it can know whether the point is the noise point,the edge point or the chaotic area.So that all the unsuitable points are processed,greatly improving the accuracy of segmentation.In order to reduce the high time complexity of the two-dimensional histogram,the algorithm reduces the dimension,and the histogram is reconstructed into one dimension histogram.Finally,the experimental results show that the algorithm makes full use of the information in space,and improves the accuracy of multi-threshold Otsu.The algorithms proposed in this paper take full advantage of histogram information and achieves better segmentation effect based on histogram operation.The algorithms can solve the shortcomings of traditional multi-threshold segmentation algorithm,such as high time complexity and interference information in multi-threshold Otsu algorithm.We can use the advantages of this algorithm to improve efficiency and accuracy in medical diagnosis.