A novel ant colonies approach to medical image segmentation
Anissa Selmani, Hassene Seddik, Ezzedine Ben Braiek · 2017
Prostate cancer is becoming a threat to humanity. Today, the diagnosis of diseases is still be realized mostly by manual methods. Nevertheless, this traditional process is inefficient and not accurate. Its precision depends on the operator's expertise. Thus, applying machine learning algorithms for malignant cells detection and counting remains a significant purpose in medical image analysis research. In this paper, we apply a modified ACO algorithm to measure the rate of cell growth of cancer's patient automatically due to segmentation and counting process. The proposed method was applied on several medical images obtained from MRI-guided prostate biopsies. The robustness of this idea was showed by comparison with hand-labeled obtained segmentation results.