An efficient CAD system for ALL cell identification from microscopic blood images
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An efficient CAD system for ALL cell identification from microscopic blood images Zhana Fidakar Mohammed 1 & Alan Anwer Abdulla 2 Received: 25 October 2019 / Revised: 10 September 2020 / Accepted: 7 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Computer-aided diagnosis (CAD) becomes a common tool for identifying diseases, especially various cancers, from medical images. Thus, digital image processing plays a significant role in this research area. This paper concerns with developing an efficient automatic system for the identification of acute lymphoblastic leukemia (ALL) cells. The proposed approach involves two steps. The first step focuses on segmenting the white blood cells (WBCs). In the second step, significant features such as shape, geometrical, statistical, and discrete cosine transform (DCT) are extracted from the segmented cells. Various classification techniques are applied to the extracted features to classify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been evaluated via extensive experiments conducted on the well-known ALL-IDB dataset of microscopic images of blood. The experimental results demonstrate that the proposed approach realizes an accuracy rate 97.45% and outperforms other existing approaches. Keywords CAD . Medical image . Leukemia . ALL . DCT . Classifier technique
1 Introduction A computer-aided diagnosis (CAD) system is a computer-based system that facilitates medical professionals in the diagnosis of diseases, especially cancers, which are processes abnormal * Alan Anwer Abdulla [email protected] Zhana Fidakar Mohammed [email protected]
1
Department of Information Technology, College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
2
Department of Information Technology, College of Commerce, University of Sulimani, Sulaymaniyah, Iraq
Multimedia Tools and Applications
cell growth in the human body, from medical images such as X-ray, MRI, CT, ultrasound, and microscopic images. The objective of the development of automatic CAD systems is the extraction of the targeted diseases with higher accuracy and decreased cost as well as time consumption compared to traditional systems. A CAD system consists of four main steps: preprocessing, segmentation, feature extraction, and classification. Therefore, digital image processing tools become increasingly important for processing medical images and detecting abnormality from these images. Furthermore, CAD systems can be used to detect various types of cancer cells from medical images such as breast cancer, brain tumor, lung cancer, skin cancer, and blood cancer. This paper focuses on blood cancer, namely leukemia, which is a Greek word that means white blood cells [8]. Leukemia considers as a type of cancer that affects the blood, lymphocyte system, and bone marrow. Blood consists of the following components: white blood cells (WBCs), red blood cells (RBCs), platelets, and plasma [2, 8]. The RBCs are res
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