A Region Proposal Approach for Cells Detection and Counting from Microscopic Blood Images

In this paper, we propose a novel and efficient method for detecting and quantifying red cells from a microscopic blood image. The proposed system is based on a region proposal approach, namely the Edge Boxes, considered as the state-of-art region proposal method. Incorporating knowledge-based constraints into the detection process by Edge Boxes we can find cells proposals rapidly and efficiently. Experimental results on a well-known public dataset show both improved accuracy and increased over the state-of-art.

[1]  Vassili Kovalev,et al.  AUTOMATIC OBJECT DETECTION AND SEGMENTATION OF THE HISTOCYTOLOGY IMAGES USING RESHAPABLE AGENTS , 2013 .

[2]  C. Lawrence Zitnick,et al.  Structured Forests for Fast Edge Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[3]  Mariya Yeldhos Red blood cell counter using embedded image processing techniques , 2018 .

[4]  Luigi Cinque,et al.  Decomposition of two-dimensional shapes for efficient retrieval , 2009, Image Vis. Comput..

[5]  Bernt Schiele,et al.  How good are detection proposals, really? , 2014, BMVC.

[6]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[7]  Sanaullah Khan,et al.  An Accurate and Cost Effective Approach to Blood Cell Count , 2012 .

[8]  Khairuddin Omar,et al.  Automatic Detection and Quantification of WBCs and RBCs Using Iterative Structured Circle Detection Algorithm , 2014, Comput. Math. Methods Medicine.

[9]  Hai-Quan Vu,et al.  Cell Splitting with High Degree of Overlapping in Peripheral Blood Smear , 2011 .

[10]  Cecilia Di Ruberto,et al.  A Computer-Aided System for Differential Count from Peripheral Blood Cell Images , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[11]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Cecilia Di Ruberto,et al.  Accurate Blood Cells Segmentation through Intuitionistic Fuzzy Set Threshold , 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

[13]  A. G. Ramakrishnan,et al.  Automation of differential blood count , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[14]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Cecilia Di Ruberto,et al.  MP-IDB: The Malaria Parasite Image Database for Image Processing and Analysis , 2018, SaMBa@MICCAI.

[16]  Udesang K. Jaliya,et al.  Segmentation and Counting of WBCs and RBCs from Microscopic Blood Sample Images , 2016 .

[17]  Cecilia Di Ruberto,et al.  Comparison of Statistical Features for Medical Colour Image Classification , 2015, ICVS.

[18]  Hamdan O. Alanazi,et al.  An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold , 2010 .

[19]  Vincenzo Piuri,et al.  All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.

[20]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[21]  Cecilia Di Ruberto,et al.  Leucocyte classification for leukaemia detection using image processing techniques , 2014, Artif. Intell. Medicine.