Classifier ensemble for an effective cytological image analysis

• A novel medical decision support framework for cytological image analysis.
• An in-depth examination of four proposed hybrid image segmentation methods.
• An efficient classifier ensemble using a trained fuser based on discriminants.
• Decision rules for fusing the outputs for nine images into a single prediction for the patient.
• Experimental investigations carried on a large and diverse database collected by authors.

Breast cancer is the most common type of cancer among women. As early detection is crucial for the patient’s health, much attention has been paid to the development of tools for effective recognition of this disease. This article presents an application of image analysis and classification methods for fine needle biopsy. In our approach, each patient is described by nine microscopic images taken from the biopsy sample. The images are related to regions of the biopsy that seem interesting to the physician who selects them arbitrarily. We propose four different hybrid segmentation algorithms dedicated to processing these images and examine their effectiveness for the nuclei feature extraction task. Classification is carried out with the usage of a classifier ensemble based on the Random Subspaces approach. To boost its effectiveness, we use a linear combination of the support functions returned by the individual classifiers in the ensemble. In the proposed medical support system, the final decision about the patient is delivered after a fusion of nine separate outputs of the classifier – each for a different image. Experimental results carried out on a diverse dataset collected by the authors prove that the proposed solution outperforms state-of-the-art classifiers and shows itself to be a valuable tool for supporting day-to-day cytologist’s routine.