Paper Contents
Abstract
ABSTRACT: Manual cell counting using Hemocytometer is commonly used to quantify cells, as it is an inexpensive and versatile method. However, it is labour-intensive, tedious, and time-consuming. On the other hand, most automated cell counting methods are expensive and require experts to operate. Thus, the use of image analysis software allows one to access low-cost but robust automated cell counting. This paper proposes a novel approach for automatic nuclei cell counting from histological images, based on effective image processing methods. Current systems are based on color or grayscale images, leading to inaccurate results and limitations. The new techniques include image thresholding, morphological image processing operations, and a connected component algorithm. Experimental results show high accuracy compared to previous works, indicating the effectiveness of the proposed approach in terms of accuracy and F1-Score.
Copyright
Copyright © 2024 Arumalla Raja. This is an open access article distributed under the Creative Commons Attribution License.
