Adaptive Successive Erosion-based Cell Image Segmentation for p53 Immunohistochemistry in Bladder Inverted Papilloma

Cell nuclei segmentation is a critical issue in automatic cell analysis for cancer diagnosis and prognosis. Marker-controlled watershed segmentation algorithm is used the most commonly. In this paper, adaptive successive erosion-based (ASE) marker extraction method for watershed algorithm is presented, with the goal of extracting markers labelling each individual nucleus, including overlapping cell nuclei. Based on the new marker detection method, an integrated cell image segmentation algorithm is developed for p53 immunohistochemistry in bladder inverted papilloma. Experiments were performed on a number of images, and results demonstrate that the algorithm produces more accurate segmentation than other methods

[1]  Hubert Cardot,et al.  Cooperation of color pixel classification schemes and color watershed: a study for microscopic images , 2002, IEEE Trans. Image Process..

[2]  J J Vaquero,et al.  Applying watershed algorithms to the segmentation of clustered nuclei. , 1998, Cytometry.

[3]  Andrew G. Dempster,et al.  Analysis of infected blood cell images using morphological operators , 2002, Image Vis. Comput..

[4]  D. Luo,et al.  Iterative Multilevel Thresholding and Splitting for Three-Dimensional Segmentation of Live Cell Nuclei Using Laser Scanning Confocal Microscopy , 1998 .

[5]  B. Macq,et al.  Morphological feature extraction for the classification of digital images of cancerous tissues , 1996, IEEE Transactions on Biomedical Engineering.

[6]  Thomas Würflinger,et al.  Robust automatic coregistration, segmentation, and classification of cell nuclei in multimodal cytopathological microscopic images. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[7]  Wei Xing Wang,et al.  Binary Image Segmentation Of Aggregates Based On Polygonal Approximation And Classification Of Concavities , 1998, Pattern Recognit..

[8]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.

[9]  Philippe Salembier.  Structuring element adaptation for morphological filters , 1992, J. Vis. Commun. Image Represent..

[10]  William Nick Street,et al.  An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition , 2003, IEEE Trans. Neural Networks.

[11]  Qingmin Liao,et al.  An accurate segmentation method for white blood cell images , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[12]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[13]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..