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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Nongmeikapam, Kishorjit
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Techniques of image segmentation: a review Meinam, Sharmila; Nongmeikapam, Kishorjit; Singh, N. Basanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp830-844

Abstract

Image segmentation is a popular topic of research. Image segmentation divides an image into different parts that can be used for further analysis. By doing so, the image becomes simple and more meaningful information can be extracted. The segmentation techniques divide an image into multiple parts based on certain features of the image namely: color, texture, and intensity value of the pixel. Segmentation is considered as one of the toughest tasks for extracting features from an image, detection of objects and lastly classification of the image. The applications of image segmentation in every aspect of life such as satellite image analysis, object detection and recognition, in agricultural field, self-driving vehicles, and medical imaging. Has become indispensable. Till date, though researchers have developed many segmentation techniques, they are unable to design a generalized methodology for the image segmentation problems. A review of image segmentation techniques has been presented in this study. A summary of the advantages and disadvantages of these techniques has been presented. The focus of this manuscript is to provide a summary of the available research work on segmentation which will benefit the enthusiastic researchers in gaining better understanding about segmentation models in various application domains.
Classification of voice pathologies using one dimensional feature vector and two dimensional scalogram Khumukcham, Ranita; Meinam, Sharmila; Nongmeikapam, Kishorjit
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp654-666

Abstract

Most research work focus only on binary classification of voice pathologies such as normal and pathological classification. However, the current work gives importance to multiclass classification too. The paper compares onedimensional (1D) feature vectors based machine learning (ML) techniques and two-dimensional (2D) scalogram image based deep learning (DL) model for binary and multiclass classification of voice pathology. The multiclass classification classifies the voice signal into four categories which are healthy, hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis. The current work demonstrates the evaluation of 1D feature vectors extracted from speech signal such as MFCC (mel-frequency cepstral coefficient) and pitch with various ML techniques like K-nearest neighbor (KNN), Naïve Bayes, and discriminant analysis (DA). Another technique that uses time-frequency scalograms derived using three different wavelets, i.e., analytical Morlet (amor), Bump, and Morse, are used for training a pretrained GoogleNet architecture, which is a very popular DL model. Experimental results show that 2D scalogram image based DL model for binary (96.05%) and multiclass (89.8%) classification of voice pathology gives better performance while comparing with 1D feature vectors based ML techniques.
Co-Authors Khumukcham, Ranita Meinam, Sharmila Singh, N. Basanta