A seeding-searching-ensemble method for gland segmentation in H&E-stained images

BACKGROUND: Glands are vital structures 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 helps their analysis and visualization by pathologists in microscopic detail. METHODS: 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: graph search, ensemble, feature extraction, and classification. Our method is computationally fast, preserves gland boundaries robustly and detects glands accurately. RESULTS: We tested the performance of our gland detection and segmentation method by analyzing a dataset of over 1700 glands in digitized high resolution clinical histology images obtained from normal and diseased human intestines. The experimental results show that our method outperforms considerably the state-of-the-art methods for gland segmentation and detection. CONCLUSIONS: Our method can produce high-quality segmentation and detection of non-overlapped glands that obey the natural property of glands in histology tissue images. With accurately detected and segmented glands, quantitative measurement and analysis can be developed for further studies of glands and computer-aided diagnosis.