Purpose: This work applies Lowe's Scale Invariant Feature Transform (SIFT) to detect micro‐calcification on mammograms. The objective of this study is to expand the function of SIFT, which has originally been used to match objects by matching the detected feature points, and to provide a new method for micro‐calcification detection. Methods: First, variables in SIFT, the scaling factor between levels of the image, the radiuses of the areas for maximum comparison within current scale and neighboring scales, and the threshold value for maximum search, were adjusted to allow nearly all the micro‐calcification to be detected as the feature points. Second, to reject feature points which are not micro‐calcification, four features of sixty‐five feature points, determined by physicians as micro‐calcification, curvature of scale space, elements of Hessian matrix, used for the discrimination of prominence and shapes, and Contrast to neighboring pixels, Size on image used to reject points on blob‐like dense tissue, were analyzed to determine the specific ranges for selecting feature points on micro‐calcification. Results: Ninety region of interest (ROI) images (268 × 268 pixel) selected from 85 mammograms (3,540 × 4,740 pixel) were employed to test the proposed method. Of the 90 ROI images, 30 images were biopsy‐verified by a physician to present a cluster of micro‐calcification. The other 60 ROI images are selected from normal mammograms. The performance of the study is evaluated by a receiver operating characteristic curve (ROC). An area under the ROC curve of 94.2%, sensitivity of 93.3%, and specificity of 95% was achieved. Conclusion: The proposed system based on SIFT accurately detects micro‐calcification in mammograms with various brightness, size, and breast density, without preprocessing them. Our findings merit further investigation for its potential to classify benign and malignant calcification.

Purpose: In positron emission tomography (PET), the single scatter simulation (SSS) algorithm is widely used for scatter estimation in clinical scans. However, bias usually occurs at the essential steps of scaling the computed SSS distribution to real scatter amounts by employing the scatter‐only projection tail. The bias can be amplified when the scatter‐only projection tail is too small, resulting in incorrect scatter correction. To this end, we propose a novel scatter calibration technique to accurately estimate the amount of scatter using pre‐determined scatter fraction (SF) function instead of the employment of scatter‐only tail information. Methods: As the SF depends on the radioactivity distribution and the attenuating material of the patient, an accurate theoretical relation cannot be devised. Instead, we constructed an empirical transformation function between SFs and average attenuation coefficients based on a serious of phantom studies with different sizes and materials. From the average attenuation coefficient, the predicted SFs were calculated using empirical transformation function. Hence, real scatter amount can be obtained by scaling the SSS distribution with the predicted SFs. The simulation was conducted using the SimSET. The Siemens Biograph™ 6 PET scanner was modeled in this study. The Software for Tomographic Image Reconstruction (STIR) was employed to estimate the scatter and reconstruct images. The EEC phantom was adopted to evaluate the performance of our proposed technique. Results: The scatter‐corrected image of our method demonstrated improved image contrast over that of SSS. For our technique and SSS of the reconstructed images, the normalized standard deviation were 0.053 and 0.182, respectively; the root mean squared errors were 11.852 and 13.767, respectively. Conclusion: We have proposed an alternative method to calibrate SSS (C‐SSS) to the absolute scatter amounts using SF. This method can avoid the bias caused by the insufficient tail information and therefore improve the accuracy of scatter estimation.