Algorithms for clustering data
Algorithms for clustering data
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Computer-Aided Evaluation of Protein Expression in Pathological Tissue Images
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Joint co-clustering: Co-clustering of genomic and clinical bioimaging data
Computers & Mathematics with Applications
Proceedings of the 30th DAGM symposium on Pattern Recognition
Seeded watersheds for combined segmentation and tracking of cells
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Unsupervised segmentation based on robust estimation and color active contour models
IEEE Transactions on Information Technology in Biomedicine
Level set analysis for leukocyte detection and tracking
IEEE Transactions on Image Processing
Efficient energies and algorithms for parametric snakes
IEEE Transactions on Image Processing
An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue
IEEE Transactions on Image Processing
Computer-aided techniques for chromogenic immunohistochemistry: Status and directions
Computers in Biology and Medicine
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This paper presents two automated methods for the segmentation of immunohistochemical tissue images that overcome the limitations of the manual approach as well as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies.