Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework

• Published in: Computer methods and programs in biomedicine Vol. 107; no. 3; pp. 497 - 512
• Main Authors: Bergmeir, Christoph, García Silvente, Miguel, Benítez, José Manuel
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.09.2012; Elsevier
• Subjects: Algorithms; Automation; Biological and medical sciences; Cell Nucleus - metabolism; Cervical cell imaging; Cytoplasm - metabolism; Early Detection of Cancer - methods; Female; High-resolution microscopic imaging; Humans; Image Processing, Computer-Assisted - methods; Internal Medicine; Internet; Medical sciences; Models, Statistical; Nucleus segmentation; Other; Pattern Recognition, Automated - methods; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Signal Processing, Computer-Assisted; Software; Technology. Biomaterials. Equipments. Material. Instrumentation; Uterine Cervical Neoplasms - diagnosis; Uterine Cervical Neoplasms - physiopathology; Nucleus segmentation; High-resolution microscopic imaging; Cervical cell imaging; High-resolution microscopic; Image resolution; Segmentation; Image processing; imaging; Biomedical engineering
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2011.09.017

► A system for semi-automatic processing of full-resolution whole-slide scans.► A new algorithm for segmenting the nuclei under control of the expert user.► Data storage and interaction of technical and medical experts is facilitated.► Open source software with web-based architecture.. In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.