Content-based classification of images for liver cancer diagnosis
Date of Publication
2007
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Jose Ronello T. Bartolome
Defense Panel Chair
Nelson Marcos
Defense Panel Member
Rigan Ap-apid
Abstract/Summary
One well known liver disease is cancer of the liver. Since the current manual process of diagnosing the disease take time and is prone to subjectivity, the group aimed to perform a comparative analysis on the different image processing techniques and identity those that is/are suitable for liver cancer detection. The inputs used were histopathological images and the three (3) computational steps were allowed: processing, feature extraction and diagnosis. Each step included different image processing techniques. The output was a report containing the diagnosis and features identified in the image as a result of the image processing techniques used. The features extracted from the input images were the morphological, topological, fractal, intensity and textural features. The result of the experimental showed that the extraction of intensity feature of the image is the best among the five (5) features since the use of the said feature enabled the system to produce a %TP of 92.31% and a %TN of 93.33%.
Abstract Format
html
Language
English
Format
Accession Number
TU14623
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Recommended Citation
Bainto, L. P., Hizon, E. H., Tan, M. D., & Uy, J. C. (2007). Content-based classification of images for liver cancer diagnosis. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/14414
