Computer assisted detection of liver neoplasm (CADLN)
Shrinivas Bhosale1, Ashish Aphale, Isaac Macwan
1Department of Biomedical Engineering, University of Bridgeport.
Summary
Radiologists manually evaluate liver neoplasm images. A new image processing algorithm aids in measuring neoplasm growth and liver volume, assisting treatment planning.
Area of Science:
- Medical Imaging
- Radiology
- Computational Biology
Background:
- Current methods for evaluating liver neoplasms rely on manual analysis by radiologists.
- There is a growing need for automated image processing tools to detect and quantify early-stage liver neoplasm growth.
Purpose of the Study:
- To develop a multifunctional image processing algorithm for measuring early liver neoplasm growth and liver volume.
- To provide radiologists with computer-generated volumetric data for serial imaging comparisons.
Main Methods:
- Development of a novel, multifunctional image processing algorithm.
- Integration of the algorithm into a system (CADLN) for volumetric data generation and comparison.
Main Results:
- The algorithm enables accurate measurement of early neoplasm growth and liver volume.
- Computer-generated volumetric data facilitates the assessment of neoplasm progression or regression over time.
Conclusions:
- The developed algorithm and system (CADLN) can significantly aid radiologists in assessing liver neoplasm status.
- This tool supports improved treatment planning for liver neoplasms through objective volumetric analysis.
