Abstract:
Microscopic image processing is critical aspects to biomedical image analysis, and blood cell counts are very important role in medical diagnoses. Various dyeing methods ...Show MoreMetadata
Abstract:
Microscopic image processing is critical aspects to biomedical image analysis, and blood cell counts are very important role in medical diagnoses. Various dyeing methods and microscopes are used, so we need methods that can effectively count cells by adapting to such diversity. This paper presents a new method that extracts the contours of red blood cells based on the quality of a binary image that is preprocessed using PCNN. The method solves the various blood smear issues caused by the different cell dyeing methods. Moreover, it uses a self-adapting method for counting cells, using the circular Hough transform(CHT) for different amplifications. The experimental results show that the proposed method performed better in contrast variations between cells and background. The method is also much more efficient in segmentation on overlapped cells, and much more accurate in counting RBC results.
Date of Conference: 15-18 December 2016
Date Added to IEEE Xplore: 19 January 2017
ISBN Information: