Study Of Thresholding Segmentation Methods Based On The Image Uncertainty Information

Image segmentation is the key step of the image processing to image analysis andplays an important role in image processing technology. There has no universal imagesegmentation method in the world at present and it’s still a classical worldwide proplem.Thresholding is one of the most commonly used methods and is the hot off the press inimage segmentation with characteristics of simple, intuitive and easy to be realized.Considering the uncertainty of the image information, it’s a difficult problem that howto deal with the uncertainty in the image and get more accurate segmentation results.The uncertainty of the image includes the randomness, fuzziness, incompleteness,instability and inconsistency by the process of image acquisition, transmission, andstorage etc. This paper discussed the problem and the shortcoming in the existingthresholding method and proposed some new thresholding algorithms with betterperformance based on the statistical information, fuzzy information and roughinformation in the image. Main research results are as follows,1. Considering the classical Otsu method which uses the statistics information of theimage failed if the histogram is unimodal or close to unimodal, two modified Otsumethod were proposed. One novel method weighs the objective function of Otsumethod with the neighborhood gray level of the threshold, and selects a thresholdvalue that has small probabilities in its neighborhood area and also maximizes thebetween-classes variance in the gray-level histogram. The other new method weighsthe objective function of the Otsu method with the gray level and gradient mapping(GGM) function. It combines the gradient information to the objective function ofthe Otsu method and makes the optimal threshold near at the boundary of the objectand the background in an image.2. Two dimensional methods were presented for the minimum error thresholdingmethod and the minimum cross entropy method, which utilize the statisticsinformation of the image. And discarding the hypothesis that sum of the probabilityin the back diagonal area in the2D histogram are zero, two dimensional linear typeminimum error thresholding method and two dimensional linear type minimumcross entropy method were proposed.3. A fast algorithm for the maximum fuzzy entropy thresholding method based on thefuzzy information of the image is presented. The new algorithm reduces the timecomplexity of the maximum fuzzy entropy thresholding method fromO (L4)toO (L3)based on the two properties of the S-type membership function and the fuzzy entropy. Based on the dual conception of the fuzzy entropy, the maximumfuzzy energy image thresholding method was discussed. To enhance theperformance of the maximum fuzzy energy thresholding method, a weightedmethod was proposed.4. The generalized fuzzy entropy thresholding method used fuzzy information of theimage was studied. And an adaptive preferences algorithm for the patameter m ofthe generalized fuzzy entropy thresholding method is proposed by the optimizationalgorithm. The new algorithm can select the parameter m for an image adaptivelyand find the optimal parameter combination (a, b, d)with the optimizationalgorithm fastly. To improve the noise immunity of the generalized fuzzy entropythresholding method, the two dimensional generalized fuzzy entropy thresholdingmethod is suggested by the definition of the two dimensional fuzzy membershipfunction for an image. Two dimensional method can remove the Gassion noiseeffectively by considering not only the gray value but also the average gray value ofthe neighbourhood.5. In the last section, we discussed the rough entropy thresholding method whichutilized the rough information of the image. The minimum square rough entropy isgiven for the expression problem of the existing rough entropy. The optimalthreshold of the minimum square rough entropy thresholding method is at the grayvalue that the roughness of the object and the background are zeros. To considermore information of the image, the two dimensional rough entropy thresholdingmethod is presented based on the two dimensional rough model of the image withthe spatial information.