Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection
Abstract of Robust Cell Detection of Histopathological Brain Tumor Images
Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection.Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis.
In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning.
The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms.
The proposed method achieves the best cell detection accuracy with a F1score = 0.96.
Conclusion
In this paper, we have proposed a general, automatic cell detection algorithm using sparse reconstruction with trivial templates and adaptive dictionary learning. By computing the sparse reconstruction with trivial templates, the algorithm is robust and accurate in handling multiple cells (occlusion) in one image patch.
The proposed algorithm works well for different images containing cells exhibiting large variations in appearances and shapes.
