Image Processing Reference
In-Depth Information
CHAPTER 30
Detecting distorted and
benign blood cells using the
Hough transform based on
neural networks and decision
trees
Hany A. Elsalamony Mathematics Department, Faculty of Science, Helwan University, Cairo, Egypt
Abstract
Sickle-cell anemia is one of the most important types of anemia. This paper presents an algorithm for
detecting blood cells characteristic of sickle-cell anemia. First, I discuss the construction of an algorithm
that can be used to detect and count benign or distorted red blood cells (RBCs) in a microscopic colored
image, even if those cells are hidden or overlapped. Second, I explain the process for checking and ana-
lyzing the constructed RBC data by applying two important techniques in data mining: the neural net-
work (NN) and the decision tree. I then review experiments demonstrating that these models show high
accuracy when predicting the counts of benign or distorted cells. In these experiments, the algorithm has
segmented around 99.98% of all input cells, helping to improve the diagnosis of sickle-cell anemia. The
NN has shown a 96.9% agreement with the algorithm's prediction outcomes, and the classification and
regression tree has achieved 92.9%.
Keywords
Sickle-cell anemia
Image watershed segmentation
Red blood cell detection and counting
C&R tree
Neural network
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