title: Morphological and textural analysis of centroblasts in low-thickness sliced tissue biopsies of follicular lymphoma.
abstract: This paper presents a new method for discriminating centroblast (CB) from non-centroblast cells in microscopic images acquired from tissue biopsies of follicular lymphoma. In the proposed method tissue sections are sliced at a low thickness level, around 1-1.5 μm, which provides a more detailed depiction of the nuclei and other textural information of cells usually not distinguishable in thicker specimens, such as 4-5 μm, that have been used in the past by other researchers. To identify CBs, a morphological and textural analysis is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. The generated feature vector is then used as input in a two-class SVM classifier with ε-Support Vector Regression and radial basis kernel function. Experimental results with an annotated dataset consisting of 300 images of centroblasts and non-centroblasts, derived from high-power field images of follicular lymphoma stained with Hematoxylin and Eosin, have shown the great potential of the proposed method with an average detection rate of 97.44%.
authors.forename: Emmanouil
authors.initials: E
authors.lastname: Michail
authors.forename: Kosmas
authors.initials: K
authors.lastname: Dimitropoulos
authors.forename: Triantafyllia
authors.initials: T
authors.lastname: Koletsa
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kostopoulos
authors.forename: Nikos
authors.initials: N
authors.lastname: Grammalidis
mesh_terms: D000465:Algorithms; D001706:Biopsy; D002466:Cell Nucleolus / Q000473:pathology; D048430:Cell Shape; D042783:Endothelial Cells / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008224:Lymphoma, Follicular / Q000175:diagnosis* / Q000473:pathology*