Axonal Structure Identification Using a Graph Transversal and Path Search Algorithm
Session Number
Project ID: CMPS 27
Advisor(s)
Dr. Gregory W. Schwartz, Northwestern University, Feinberg School of Medicine,
Discipline
Computer Science
Start Date
17-4-2024 10:00 AM
End Date
17-4-2024 10:15 AM
Abstract
ImageJ is a software platform for open-source image analysis that has assisted researchers with various image analysis applications. Its success has been primarily attributed to supportive and cooperative developer and user communities. ImageJ is also useful for data segmentation, which assigns a label to every pixel in an image such that images with the same label share similar qualities. Segmenting objects is the first step in tracking analysis, which allows researchers to follow the segmented objects' movements over time. Many advanced tools and techniques, including Trainable WEKA Segmentation (TWS) and Labkit, use machine learning to implement pixel classification functionality through the open WEKA library for segmentation. The data used in this study underwent immunohistochemical staining and revealed axonal structures in three separate panels. This paper presents a qualitative analysis of another Fiji plugin, PathFinder, that utilizes a graph transversal and path search algorithm called A* and uses heuristics to estimate the most efficient path, alongside TWS and LabKit.
Axonal Structure Identification Using a Graph Transversal and Path Search Algorithm
ImageJ is a software platform for open-source image analysis that has assisted researchers with various image analysis applications. Its success has been primarily attributed to supportive and cooperative developer and user communities. ImageJ is also useful for data segmentation, which assigns a label to every pixel in an image such that images with the same label share similar qualities. Segmenting objects is the first step in tracking analysis, which allows researchers to follow the segmented objects' movements over time. Many advanced tools and techniques, including Trainable WEKA Segmentation (TWS) and Labkit, use machine learning to implement pixel classification functionality through the open WEKA library for segmentation. The data used in this study underwent immunohistochemical staining and revealed axonal structures in three separate panels. This paper presents a qualitative analysis of another Fiji plugin, PathFinder, that utilizes a graph transversal and path search algorithm called A* and uses heuristics to estimate the most efficient path, alongside TWS and LabKit.