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A Texture Feature Ranking Model for Predicting Survival Time of Brain Tumor Patients
- Source :
- SMC
- Publication Year :
- 2013
- Publisher :
- IEEE, 2013.
-
Abstract
- Automated prediction of patient-specific disease progression can significantly contribute to clinical treatment. This paper presents a computer-assisted framework to tackle the survival time prediction problem. Inspired by the assumption that niche tumor regions may play a significant role in cancer diagnosis, we explore local visual variations from multiple MRI sequences. The research consists of three parts: 1) the extraction of multi-scale Local Binary Patterns (LBP) to describe the visual variations, 2) a supervised forward feature selection approach, called the Feature Ranking Model (FRM) which captures single feature predictive ability efficiently, and combines the top features to form a feature subset, 3) We cast the clinical survival time prediction task as a binary category classification problem. We tested the framework using a dataset of 32 cases collected from The Cancer Genome Atlas (TCGA). We obtained a 93.75% accuracy rate for the prediction of survival time.
- Subjects :
- Contextual image classification
Computer science
Local binary patterns
business.industry
Feature extraction
Cancer
Feature selection
Pattern recognition
Machine learning
computer.software_genre
medicine.disease
Ranking (information retrieval)
Support vector machine
Image texture
Ranking
Feature (computer vision)
medicine
Artificial intelligence
business
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2013 IEEE International Conference on Systems, Man, and Cybernetics
- Accession number :
- edsair.doi...........bda25e6d31dce612fd9b46ee2b80e974