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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Hizmawati Madzin
Universiti Putra Malaysia
Electrical & Electronics Engineering

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An algorithm to measure unsymmetrical circle shape of intravascular ultrasound image using image processing techniques Suhaili Beeran Kutty; Rahmita Wirza O. K. Rahmat; Sazzli Shahlan Kassim; Hizmawati Madzin; Hazlina Hamdan
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2694

Abstract

In diagnosing coronary artery disease, measurement of the cross-sectional area of the lumen, maximum and minimum diameter is very important. Mainly, it will be used to confirm the diagnosing, to predict the stenosis if any and to ensure the size of the stent to be used. However, the measurement only offers by the existing software and some of the software needs human interaction to complete the process. The purpose of this paper is to present the algorithm to measure the region of interest (ROI) on intravascular ultrasound (IVUS) using an image processing technique. The methodology starts with image acquisition process followed by image segmentation. After that, border detection for each ROI was detected and the algorithm was applied to calculate the corresponding region. The result shows that the measurement is accurate and could be used not only for IVUS but applicable to solid circle and unsymmetrical circle shape. 
Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN Nur Sholehah Mat Said; Hizmawati Madzin; Siti Khadijah Ali; Ng Seng Beng
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 3: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i3.pp1523-1533

Abstract

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.
Co-Authors Hazlina Hamdan Ng Seng Beng Nur Sholehah Mat Said Rahmita Wirza O. K. Rahmat Sazzli Shahlan Kassim Siti Khadijah Ali Suhaili Beeran Kutty