Segmentation and counting of blood cells are considered as an important step that helps to extract features to diagnose some specific diseases like malaria or leukemia. The manual counting of white blood cells (WBCs) and red blood cells (RBCs) in microscopic images is an extremely tedious, time consuming, and inaccurate process. Automatic analysis will allow hematologist experts to perform faster and more accurately. The proposed method uses an iterative structured circle detection algorithm for the segmentation and counting of WBCs and RBCs. The separation of WBCs from RBCs was achieved by thresholding, and specific preprocessing steps were developed for each cell type. Counting was performed for each image using the proposed method based on modified circle detection, which automatically counted the cells. Several modifications were made to the basic (RCD) algorithm to solve the initialization problem, detecting irregular circles (cells), selecting the optimal circle from the candidate circles, determining the number of iterations in a fully dynamic way to enhance algorithm detection, and running time. The validation method used to determine segmentation accuracy was a quantitative analysis that included Precision, Recall, andF-measurement tests. The average accuracy of the proposed method was 95.3% for RBCs and 98.4% for WBCs.

Firefly Algorithm (FA) is one of the new natural inspired optimization algorithms. It is inspired by the flashing behavior of the fireflies. Firefly algorithm, has some drawbacks such as getting trapped into several local optima, FA parameters are set fixed without change during iterations time. Besides that, it does not memorize or remember the history of any situation for each iteration. In this paper, we propos a firefly photinus algorithm (FPA) based on the initialize mate list to solve problems of trapped into several local optima and remember history of situation to forbidden fireflies movements in mate list (history) during the search process, and propose new absorption parameter σ to change the parameters during iterations time which lead to balance between exploration and exploitation, and it controls the dominance area of a lighter firefly during time iterations by reduction or increase σ coefficient whether. The experimental results tested on thirteen benchmark functions are selected to evaluate performance of the FPA and to compare it with the standards of the FA and Some FA variants algorithm, it show that FPA algorithm can outperform FA and FA variants algorithm in most of the experiments.

Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with thek-means and fuzzyc-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.