A Framework for Segmenting Filarial Worm in Thick Blood Smear Images Using Morphological Operations Baskaran Sharmila, Krishnamurthy Kamalanand, Raju Lourduraj John De Britto Brazilian Archives of Biology and Technology, 2025 Filariasis is a parasitic disease caused by thread-like nematode worms known as filarial worms. This disease is transmitted to humans through bites of infected mosquitoes. Filariasis is a significant public health concern in many tropical and subtropical regions of the world, particularly in Africa, Asia, and the Pacific islands. It causes the clinical disease namely Lymphatic Filariasis that primarily affects the lymphatic system and leads to lymphedema, elephantiasis, and recurrent fever. A global initiative to eradicate lymphatic filariasis as an international health problem has been launched by the World Health Organization (WHO). In this work, the acquired microscopic blood smear images were preprocessed and converted into grayscale images. Further, the images are subjected to morphological operations such as skeletonization, thinning and Euclidean distance transform (EDM) to extract the filarial worms from blood smear images. It is found that the similarity indices between the ground truth and the images segmented using our proposed method were high with an average Dice, Jaccard and Structural Similarity Index Measure (SSIM) of 97.56%, 97.11% and 98.21% respectively. It is observed that the proposed framework accurately segments the worm without losing its proximal and distal portions, despite the presence of artifacts, and variation in shape and size of the worms due to folding or coiling. The automated segmentation of filarial worms is highly desirable for mass screening of lymphatic filariasis, particularly during the pre-elimination phase and in low-endemic situations.
RECENT SCHOLAR PUBLICATIONS
A Framework for Segmenting Filarial Worm in Thick Blood Smear Images Using Morphological Operations B Sharmila, K Kamalanand, RLJD Britto Brazilian Archives of Biology and Technology 68, e25240963 , 2025 2025
A framework for segmentation of filarial worm in thick blood smear images using image processing techniques and machine learning algorithms B Sharmila, K Kamalanand, RLJ De Britto Biomedical Signal Processing and Control 108, 107881 , 2025 2025 Citations: 1
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Relational Database Management Systems SR BSharmila,VJanaki ISBN: 978-93-91332-21-1, VR1 Publications 1, 1-328 , 2022 2022
A framework for segmentation of filarial worm in thick blood smear images using image processing techniques and machine learning algorithms B Sharmila, K Kamalanand, RLJ De Britto Biomedical Signal Processing and Control 108, 107881 , 2025 2025 Citations: 1
Hough’s Transform-Based IoT Device for Automated Identification and Prediction of Blood Groups V Asokan, V Ponnuswamy, S Baskaran Journal of Biomedical Physics and Engineering , 2025 2025 Citations: 1
A Framework for Segmenting Filarial Worm in Thick Blood Smear Images Using Morphological Operations B Sharmila, K Kamalanand, RLJD Britto Brazilian Archives of Biology and Technology 68, e25240963 , 2025 2025
Detection of Cataracts in Eye using Image Processing and Machine Learning Techniques AGRKK B.Sharmila, S.Arockia Sukanya Recent Trends in Instrumentation and Control (RTIC-2024) 7, 1-10 , 2024 2024
Relational Database Management Systems SR BSharmila,VJanaki ISBN: 978-93-91332-21-1, VR1 Publications 1, 1-328 , 2022 2022
