title: Extraction of informative cell features by segmentation of densely clustered tissue images.
abstract: This paper presents a fast methodology for the estimation of informative cell features from densely clustered RGB tissue images. The features estimated include nuclei count, nuclei size distribution, nuclei eccentricity (roundness) distribution, nuclei closeness distribution and cluster size distribution. Our methodology is a three step technique. Firstly, we generate a binary nuclei mask from an RGB tissue image by color segmentation. Secondly, we segment nuclei clusters present in the binary mask into individual nuclei by concavity detection and ellipse fitting. Finally, we estimate informative features for every nuclei and their distribution for the complete image. The main focus of our work is the development of a fast and accurate nuclei cluster segmentation technique for densely clustered tissue images. We also developed a simple graphical user interface (GUI) for our application which requires minimal user interaction and can efficiently extract features from nuclei clusters, making it feasible for clinical applications (less than 2 minutes for a 1.9 megapixel tissue image).
authors: Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. sk9@ gatech.edu
authors: Sonal
authors: S
authors: Kothari
authors: Qaiser
authors: Q
authors: Chaudry
authors: May D
authors: MD
authors: Wang
mesh_terms: D002292:Carcinoma, Renal Cell / Q000473:pathology; D002467:Cell Nucleus / Q000473:pathology; D053144:Cell Nucleus Shape; D053145:Cell Nucleus Size; D002477:Cells / Q000473:pathology*; D016000:Cluster Analysis; D003116:Color; D006258:Head and Neck Neoplasms / Q000473:pathology; D006801:Humans; D007680:Kidney Neoplasms / Q000473:pathology; D057054:Molecular Imaging / Q000379:methods*; D009928:Organ Specificity; D014584:User-Computer Interface