Classification of microscopy images of Langerhans islets

Evaluation of images of Langerhans islets is a crucial procedure for planning an islet transplantation, which is a promising diabetes treatment. This paper deals with segmentation of microscopy images of Langerhans islets and evaluation of islet parameters such as area, diameter, or volume (IE). For all the available images, the ground truth and the islet parameters were independently evaluated by four medical experts. We use a pixelwise linear classifier (perceptron algorithm) and SVM (support vector machine) for image segmentation. The volume is estimated based on circle or ellipse fitting to individual islets. The segmentations were compared with the corresponding ground truth. Quantitative islet parameters were also evaluated and compared with parameters given by medical experts. We can conclude that accuracy of the presented fully automatic algorithm is fully comparable with medical experts.

[1]  C. Ricordi,et al.  Evaluation of islet isolation by a new automated method (Coulter Multisizer Ile) and manual counting. , 1998, Transplantation proceedings.

[2]  Clark K. Colton,et al.  Enumeration of islets by nuclei counting and light microscopic analysis , 2010, Laboratory Investigation.

[3]  A. Naji,et al.  Validation of methodologies for quantifying isolated human islets: an islet cell resources study , 2010, Clinical transplantation.

[4]  Philippe Morel,et al.  Computer-Assisted Digital Image Analysis to Quantify the Mass and Purity of Isolated Human Islets Before Transplantation , 2008, Transplantation.

[5]  T J Fetterhoff,et al.  Morphologic analysis of pancreatic islets automated image analysis. , 1994, Transplantation proceedings.

[6]  P. Buchwald,et al.  Quantitative Assessment of Islet Cell Products: Estimating the Accuracy of the Existing Protocol and Accounting for Islet Size Distribution , 2009, Cell transplantation.

[7]  Linda G. Shapiro,et al.  Computer and Robot Vision (Volume II) , 2002 .

[8]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[9]  Václav Hlavác,et al.  Ten Lectures on Statistical and Structural Pattern Recognition , 2002, Computational Imaging and Vision.

[10]  J J O'Neil,et al.  Improved assessment of isolated islet tissue volume using digital image analysis. , 1998, Cell transplantation.

[11]  Peter Girman,et al.  Digital imaging as a possible approach in evaluation of islet yield. , 2003, Cell transplantation.

[12]  K. Mulchrone,et al.  Fitting an ellipse to an arbitrary shape: implications for strain analysis , 2004 .

[13]  Peter Buchwald,et al.  Quantification of the Islet Product: Presentation of a Standardized Current Good Manufacturing Practices Compliant System With Minimal Variability , 2011, Transplantation.

[14]  Peter J. Morris,et al.  Islet isolation assessment in man and large animals , 1990, Acta diabetologia latina.

[15]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.