Open Access

ARTICLE

Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

Mohd Annuar Isa¹, Mohamad Nur Khairul Hafizi Rohani^1,*, Baharuddin Ismail¹, Mohamad Kamarol Jamil¹, Muzamir Isa¹, Afifah Shuhada Rosmi¹, Mohd Aminudin Jamlos², Wan Azani Mustafa¹, Nurulbariah Idris³, Abdullahi Abubakar Mas’ud⁴

1 Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Perlis, Malaysia
2 School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Penang, Malaysia
3 Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Perlis, Malaysia
4 Department of Electrical and Electronics Engineering, Jubail Industrial College, Saudi Arabia

* Corresponding Author: Mohamad Nur Khairul Hafizi Rohani. Email:

Computers, Materials & Continua 2023, 75(1), 741-760. https://doi.org/10.32604/cmc.2023.036077

Received 16 September 2022; Accepted 12 November 2022; Issue published 06 February 2023

Abstract

Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation. Therefore, this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm (MSLTA) by integrating batch processing method. The proposed method, h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy. The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation. The treeing images are then sampled and binarized in pre-processing. In the next phase, segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration. Finally, the comparative investigation has been conducted using standard performance assessment metrics, including accuracy, sensitivity, specificity, Dice coefficient and Matthew’s correlation coefficient (MCC). Based on segmentation performance evaluation against several established segmentation methods, h-MSLTA achieved better results of 95.43% accuracy, 97.28% specificity, 69.43% sensitivity rate with 23.38% and 24.16% average improvement in Dice coefficient and MCC score respectively over the original algorithm. In addition, h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image. These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.

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Keywords

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