Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks analyze each system. We use machine-learning circumvent need for develop neural network both locate subhalos in an image as well determine their mass using t...

Elucidation and Improvement of Algorithms for Mass Spectrometry Isotope Trace Detection Rob Smith Department of Computer Science, BYU Doctor of Philosophy Mass spectrometry facilitates cutting edge advancements in many fields. Although instrumentation has advanced dramatically in the last 100 years, data processing algorithms have not kept pace. Without sensitive and accurate signal segmentatio...

Breast cancer is the most common with highest mortality risk among female worldwide and breast mass effective sign for identification. Thus, accurate segmentation of regarded as a key step to reduce death rate. Traditional methods require prior knowledge manually set parameters, while recent studies prefer construct neural networks based on feature reuse. However, can display in different orien...

detecting blood vessels is an important task in retinal image analysis.  the task is more challenging with the presence of bright and dark lesions in retinal images. here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. first, the negative impact of bright lesions is reduced by using k-means segmentation in a perceptive sp...

introduction two-dimensional gel electrophoresis (2dge) is a powerful technique in proteomics for protein separation. in this technique, spot segmentation is an essential stage, which can be challenging due to problems such as overlapping spots, streaks, artifacts and noise. watershed transform is one of the common methods for image segmentation. nevertheless, in 2dge image segmentation, the no...