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The leaf rolling index and thickness are important indexes of plant drought resistance. However, existing measuring methods of these two indicators are wasteful, inefficient and weak universality. To solve this problem,the minimum external rectangle method based on Graham algorithm was proposed to extract the value of leaf rolling index and the concave and convex point detection algorithm combined the corner detection was proposed to measure the leaf thickness. The algorithm used in the article had the following steps: firstly,the permanent sides were obtained by paraffin sectioning technique, and then connected the microscope and computer to obtain slice images. Secondly, a red grayscale method was proposed for the Cleistogenes songorica leaf anatomical structure image to enhance contrast and details, according to the color difference of red and blue between the foreground and the background. Then through subjective judgment (observing the change of processed image, and comparing the changes to find out which method was the best one), it was found out that images processed by grayscale methods had the shortcoming of details blurred and proposed method showed better performance in improving the quality of image and the details of the images than the other methods. Also by evaluation functions: average gradient(AG),contrast(C) and information entropy(E) were used for objectively evaluated the method used and the traditional ones. The average value of AG,C and E was got by processing 30 test images, and it turned out that the values of the method proposed was better than the other methods. On this basis, eliminated noise using morphological operation and using linear filtering to eliminate serrated boundaries and eventually segmented background and objective by the maximum interclass variance (Otsu). Through subjective judgment, it was found out that the segmented target area basically coincided with the original boundary of the target. Also by evaluation functions: false positive rate(FPR), false negative rate(FNR),and global segmentation accuracy(GSA) were used for objectively evaluating the method. The average value of FPR,FNR and GSA was got by processing 30 test images were 0.75%, 3.49% and 98.14%, respectively. Finally,according to the actual measurement mode of leaf rolling index, the longest distance between the two points on the Cleistogenes songorica leaf anatomical structure image was measured, the minimum external rectangle method based on Graham algorithm was used to extract the value of leaf rolling index of the objective. The measurement was compared with its mean value of interactive measurement by ToupTek Toupview software,the average relative error of 30 test images was 0.96% and the average time consumed was 4.87s by the measurement proposed, the speed was improved by 11 times. According to the actual measurement of leaf thickness was to measure the distance between the concave points and the concave points on the left and right boundary of the Cleistogenes songorica leaf anatomical structure image, it was also the distance between the convex points and the convex points,the proposed concave and convex point detection algorithm combined the corner detection algorithm and vector product method to eliminate the useless apexes to obtain the concave and convex points in accordance with the actual measurement. Then, the leaf thickness value was obtained by concave points matching and convex points matching.The measurement was compared with its mean value of interactive measurement by ToupTek Toupview software,the average relative error of 30 test images was 3.69% and the average time consumed was 4.92s by the measurement proposed,the speed was improved by 38 times. In conclusion, the algorithm was more suitable for background segmentation and object measurement of Cleistogenes songorica leaf slices and can also provide a reference for other plant leaf slice images.