LUNG CANCER SUBTYPE CLASSIFICATION FROM WHOLE SLIDE HISTOPATHOLOGICAL IMAGES

Author

Dheeraj Ganti

Graduation Semester and Year

2015

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Junzhou Huang

Abstract

Lung Cancer is one of the most serious diseases causing death for human be- ings. The progression of the disease and response to treatment di ers widely among patients. Thus it is very important to classify the type of tumor and also able to predict the clinical outcomes of patients. Majority of lung cancers is Non-Small Cell Lung Cancer (NSCLC) which constitutes of 84 % of all the type of lung cancers. The two major subtypes of NSCLC are Adenocarcinoma (ADC) and Squamous Cell Car- cinoma (SCC). Accurate classi cation of the lung cancer as NSCLC and its subtype classi cation is very important for quick diagnosis and treatment. In this research, a quantitative framework is proposed for one of the most challenging clinical case, the subtype recognition and classi cation of Non-Small Cell Lung Cancer (NSCLC) as Adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The proposed frame- work made e ective use of both the holistic features and topological features which are extracted from whole slide histopathology images. The local features are extracted after using vigorous cell detection and segmentation so that every individual cell is segmented from the images. Then e cient geometry and texture descriptors which are based on the results of cell detection are used to extract the holistic features. We determined the topological properties from the labelled nuclei centroids to study into the potent of the topological features. The results of the experiments from popular classi ers show that the structure of the cells plays vital role and to di erentiate be- tween the two subtypes of NSCLC, the topological descriptors act as representative markers.

Keywords

Lung cancer, Subtype recognition

Disciplines

Computer Sciences | Physical Sciences and Mathematics

License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Comments

Degree granted by The University of Texas at Arlington

Recommended Citation

Ganti, Dheeraj, "LUNG CANCER SUBTYPE CLASSIFICATION FROM WHOLE SLIDE HISTOPATHOLOGICAL IMAGES" (2015). Computer Science and Engineering Theses. 437.
https://mavmatrix.uta.edu/cse_theses/437