A seeding-searching-ensemble method for gland segmentation and detection

Glands are vital tissues found throughout the human body and their structure and function are affected by many diseases. The ability to segment and detect glands among other types of tissues is important for the study of normal and disease processes and is readily visualized by pathologists in microscopic detail. In this paper, we develop a new approach for segmenting and detecting intestinal glands in H&E stained histology images, which utilizes a set of advanced image processing techniques such as graph search, ensemble, feature extraction and classification. Our method computes fast, and is able to preserve gland boundaries robustly and detect glands accurately. We tested the performance of gland detection and segmentation by analyzing a dataset of 1723 glands from digitized high-resolution clinical histology images obtained in normal and diseased intestines. The experimental results show that our method outperforms considerably the state-of-the-art methods for gland segmentation and detection tasks.