Aimi Salihah Abdul Nasir
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Preferred name
Aimi Salihah Abdul Nasir
Official Name
Aimi Salihah, Abdul Nasir
Alternative Name
Abdul Nasir, Aimi Salihah
Abdul-Nasir, Aimi Salihah
Nasir, Aimi Salihah Abdul
Main Affiliation
Universiti Malaysia Perlis
Scopus Author ID
57219027157
Researcher ID
AAG-1747-2021

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Publication

Improving lung region segmentation based on lazy snapping and clustering for aiding COVID-19 diagnosis

2024-12 , Raihana Nur Safina Rahmad , Aimi Salihah Abdul Nasir , Wei Herng Ooi , Afifah Shuhada Rosmi

The COVID-19 global pandemic, brought on by the rapid spread of the new coronavirus (SARS-CoV-2), has developed into one of the healthcare industry's most significant challenges in recent memory. Early detection of positive patients is essential to prevent the further spread of the COVID-19 virus. Chest x-ray (CXR) images of patients reporting shortness of breath initially led clinicians to suspect the presence of this novel virus. On CXR images, among the alterations detected in the lungs are indications of cloud region, also known as Ground-Glass Opacity. Consequently, the primary objective of this study is to develop a robust segmentation and to acquire an accurate segmented lung region in a CXR image, as this is a necessary step for accurate diagnosis using computer-aided diagnostic systems (CADS). The proposed methodology employs a multi-level segmentation strategy to improve the performance of lung region segmentation, where Lazy Snapping is utilized as pre-segmentation step to automatically remove the bone of the chest area, followed by clustering to achieve the complete segmentation. Furthermore, the advantage of fast k-means (FKM) clustering has also been utilized to obtain the desired lung region. The proposed strategy using Lazy Snapping and FKM was experimented on 150 CXR images and has achieved an average accuracy, sensitivity of and specificity of 92.38%, 85.23% and 96.27%, respectively. Based on the results obtained, this approach demonstrated efficacy in lung segmentation in chest x-ray images and has a significant potential for clinical use.

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Robust segmentation of COVID-19 chest X-Ray images: analysis of variant k-means based clustering algorithms

2025-02 , Aimi Salihah Abdul Nasir , Abdul Syafiq Abdull Sukor , Ooi Wei Herng

Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers <2, 4, 6, 8, 10> of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

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