Elaboration of novel image processing algorithm for arcing discharges recognition on HV polluted insulator model

Chaou, A. K.; Mekhaldi, A.; Teguar, M.

doi:10.1109/tdei.2015.7076800

Cited by 25 publications

(12 citation statements)

References 22 publications

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“…During the image segmentation process by the OTSU method, the between-cluster variance is considered as an important index for the uniformity of gray distributions (Chaou et al 2015 ). The larger the between class variance is, the greater the difference between the two classes becomes.…”

Section: Methodsmentioning

confidence: 99%

The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization

Aggarwal

Bhamrah

Ryait³

2016

SpringerPlus

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Liver cirrhosis is considered as one of the most common diseases in healthcare. The widely accepted technology for the diagnosis of liver cirrhosis is via ultrasound imaging. This paper presents a technique for detecting the cirrhosis of liver through ultrasound images. The region of interest has been selected from these ultrasound images and endorsed from a radiologist. The identification of liver cirrhosis is finally detected through modified local binary pattern and OTSU methods. Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.

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Section: Methodsmentioning

confidence: 99%

The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization

Aggarwal

Bhamrah

Ryait³

2016

SpringerPlus

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“…4-6 show that the discharges progress into eight steps as summarised in Table 1. Such steps have been inspired by other investigations in the field [1,7].…”

Section: Discharge Flashover Processmentioning

confidence: 99%

“…They are intended to perform and operate effectively under the most severe climatic conditions. Consequently, monitoring the performance of these insulators under pollution is of the upmost importance to maintain safe and continuous operation of power on the network [1][2][3][4][5][6]. If insulators are not correctly monitored especially under severe pollution conditions, the flashover can occur through the following steps: accumulation of contamination layer, wetting of the insulator, increasing of the leakage current, the formation of dry band arcs and finally the extension of such arcs to cover the leakage path [1,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Chaou et al [1] are the sole who developed (black and white) image processing algorithm to recognise arcing discharges pattern on the insulator model through the extraction of four indicators from the filtered images. These indicators have been used as inputs of three classification algorithms, namely Knns, NBs, SVMs, to distinguish between the presence or the absence of arc discharges on the insulating surface.…”

Section: Introductionmentioning

confidence: 99%

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Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

Ferrah

Chaou

Maadjoudj

et al. 2020

IET Science, Measurement & Technology

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“…Virmani et al [28] presented CAD system for focal liver lesions in which B-mode US images were used. This system was evaluated on the images of hemangioma and metastatic carcinoma lesion, small and large hepatocellular carcinoma lesions, typical and atypical cases of cyst, and normal liver tissue [29]. The texture features were extracted through inside and outside regions of lesions.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of cirrhosis through ultrasound imaging by intensity difference technique

Aggarwal

Bhamrah

Ryait³

2019

J Image Video Proc.

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Cirrhosis is a liver disease that is considered to be among the most common diseases in healthcare. Due to its noninvasive nature, ultrasound (US) imaging is a widely accepted technology for the diagnosis of this disease. This research work proposed a method for discriminating the cirrhotic liver from normal liver through US images. The liver US images were obtained from the radiologist. The radiologist also specified the region of interest (ROI) from these images, and then the proposed method was applied to it. Two parameters were extracted from the US images through differences in intensity of neighboring pixels. Then, these parameters can be used to train a classifier by which cirrhotic region of test patient can be detected. A 2-D array was created by the difference in intensity of the neighboring pixels. From this array, two parameters were calculated. The decision was taken by checking these parameters. The validation of the proposed tool was done on 80 images of cirrhotic and 30 images of normal liver, and classification accuracy of 98.18% was achieved. The result was also verified by the radiologist. The results verified its possibility and applicability for high-performance cirrhotic liver discrimination.

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Elaboration of novel image processing algorithm for arcing discharges recognition on HV polluted insulator model

Cited by 25 publications

References 22 publications

The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization

The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization

Novel colour image encoding system combined with ANN for discharges pattern recognition on polluted insulator model

Detection of cirrhosis through ultrasound imaging by intensity difference technique

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