Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer
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Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer Raneem Qaddoura1 · Waref Al Manaseer2 · Mohammad A. M. Abushariah3 · Mohammad Aref Alshraideh2 Received: 14 June 2019 / Revised: 16 March 2020 / Accepted: 1 May 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Image segmentation is a popular technique that is used for extracting information from images, which has also gained a lot of interest lately due to its importance in different scientific fields such as the medical field. This paper proposes a novel image segmentation technique using Expectation-Maximization (EM) clustering algorithm and Grasshopper Optimizer Algorithm (GOA). The proposed technique and the concept of image segmentation are effectively applied on dental radiography datasets that are collected from 120 patients with an age between 6 to 60 years old. To validate the proposed technique, a comparison in terms of purity and entropy measures is conducted against K-means, X-means, EM, and Farthest First algorithms. Based on our experimental results, the proposed technique using EM and GOA achieved the best results compared to other algorithms for all three datasets in terms of entropy and purity. The best results were obtained using the second dataset, which achieved purity value of 0.7126 and entropy value of 0.3083. Further, the proposed technique also outperforms U-net and Random Forest algorithms for the selected datasets. Keywords Image segmentation · Expectation-Maximization algorithm · Grasshopper optimization algorithm · Dental radiography · Anatomical segmentation and classification
1 Introduction Dental therapy has recently obtained more attention as human beings have become well aware of their dental health. According to the oral health and dental care in the U.S.A., approximately 9,404 people died from cancer of the oral cavity and periodontal disease in the U.S.A in 2014, and the five-year survival rate of such cancers was 68% [27].
Raneem Qaddoura
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Dental radiography is commonly called X-rays, which is used by dentists to find cavities, damages, and diseases that are not visible during a clinical dental examination [19]. Consequently, they require special analysis and inspection. Image segmentation is a popular technique that is used as the first step in medical image analysis to reinforce the diagnosis and prognoses of many diseases including dental diseases. It is also defined as the process of dividing an image into multiple segments, which is typically used to identify objects or other relevant information in digital images [55]. Image segmentation approaches can be categorized into two approaches, namely: discontinuity detection based approach and similarity detection based approach [23]. In addition, image segmentation techniques are classified into the following categories [21]: –
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