Abstract
Congenital heart defect (CHD) is one of the most serious congenital deformities in a fetus. About 31% to 55% of CHDs are the primary cause that leads to life-threatening problem among neonates, hence sonographers emphasize the importance of prenatal CHD screening. Among 18 types of CHDs, the asymmetric appearance of the heart seems to be a challenging part. Hypoplastic left heart syndrome (HLHS) is a critical and rare CHD, with an underdeveloped left heart chamber of the fetus. This prenatal CHD can be diagnosed between 17 to 21 weeks of gestation period. Though ultrasound provides a good diagnostic result, prenatal diagnosis is still a challenging area due to its speckle noise and irregular appearance of the heart chambers. In this context, the basic step is to appropriately select the pre-processing algorithm, one such algorithm is the Fuzzy based maximum likelihood estimation technique (FMLET). Right ventricle left ventricle ratio (RVLVR) and cardiac thoracic ratio (CTR) are the two important features required for manual diagnosis of the ultrasound images. Hence, morphological operations such as open, close, thinning and thickening helps to extract the diagnostically important features inherent in the images. Finally, the computer aided decision support (CADS) system is designed with pre-processing module, morphological module and adaptive neuro fuzzy (ANFC) classifier module. ANFC is investigated as the good classifiers to help the experts in terms of self-learning with higher diagnostic rate. The proposed CADS proven with 91% of diagnostic accuracy and the standardized area under the ROC curve obtained was 0.9137.
Similar content being viewed by others
Data availability
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
References
Pouch AM, Aly AH, Lasso A, Nguyen AV, Scanlan AB (2017) Image Segmentation and Modeling of the Pediatric Tricuspid Valve in Hypoplastic Left Heart Syndrome. Funct Imaging Model Heart 10263:95–105. https://doi.org/10.1007/978-3-319-59448-4_10 Epub 2017 May 23
Lee JS (1986) Speckle suppression and analysis for synthetic aperture radar images. Opt Eng 25(5):255636
Fruitman DS (2000) Hypoplastic left heart syndrome: prognosis and management options. Paediatr Child Health 5(4):219–25. https://doi.org/10.1093/pch/5.4.219
Bellsham-Revell H (2021) Noninvasive Imaging in Interventional Cardiology: Hypoplastic Left Heart Syndrome, frontiers in cardiovascular medicine, https://doi.org/10.3389/fcvm.2021.637838, volume 8
Hoffman JI, Kaplan S (2002) The incidence of congenital heart disease. J Am Coll Cardiol 39(12):1890e1900
Gobergs R, Salputra E, Lubaua I (2016) Hypoplastic left heart syndrome: a review. Acta Medica Lituanica 23(2):86–98
Carvalho JS, Mavrides E, Shinebourne EA, Campbell S, Thilaganathan B (2002) Improving the effectiveness of routine prenatal screening for major congenital heart defects. Heart 88(4):387e391
Mohammed NB, Chinnaiya A (2011) Evolution of foetal echocardiography as a screening tool for prenatal diagnosis of congenital heart disease. J Pak Med Assoc 61(9):904–909
Frost VS, Stiles JA, Shanmugan KS, Holtzman JC (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell 2:157e166
Coupé P, Hellier P, Kervrann C, Barillot C (2009) Nonlocal means-based speckle filtering for ultrasound images. IEEE Trans Image Process 18(10):2221e2229
Sridevi S, Nirmala S (2016) Fuzzy inference rule-based image despeckling using adaptive maximum likelihood estimation. J Intell Fuzzy Syst 31(1):433e441
Ciurte A, Rueda S, Bresson X, Nedevschi S, Papageorghiou AT, Noble JA, Bach Cuadra M (2012) Ultrasound image segmentation of the fetal abdomen: a semi-supervised patch-based approach, in: Proceedings of Challenge US: Biometric Measurements from Fetal Ultrasound Images, ISBI, pp. 13e15
Nirmala S, Sridevi S (2016) Markov random field segmentation based sonographic identification of prenatal ventricular septal defect. Procedi Comput Sci 79:344–350
Aysal TC, Barner KE (2007) Rayleigh-maximum-likelihood filtering for speckle reduction of ultrasound images. IEEE Trans Med Imaging 26(5):712e727
Sadek S, Al-Hamadi A (2015) Entropic image segmentation: a fuzzy approach based on Tsallis entropy. Int J Comput Vis Signal Process 5(1):1e7
P. Soille, Morphological image analysis: principles and applications, Springer Science & Business Media, 2013
Michielsen K, De Raedt H (2001) Integral-geometry morphological image analysis. Phys Rep 347:461–538
Banon, GJF, Barrera, J, de Mendonça Braga-Neto, U (2007) Rio de Janeiro, RJ, Brazil, mathematical morphology and its applications to signal and image processing, proceedings of the 8th international symposium on mathematical, October 10–13
Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158e168. https://doi.org/10.1016/j.asoc.2014.11.012
Loizou CP, Pattichis CS, Pantziaris M, Tyllis T, Nicolaides A (2006) Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering. Med Biol Eng Comput 44(5):414
De Marsico M, Nappi M, Riccio D (2015) Entropy-based automatic segmentation and extraction of tumors from brain MRI images, in: International conference on computer analysis of images and patterns, Springer, Cham, pp. 195e206
Macedo AJ, Ferreira M, Borges A, Sampaio A, Ferraz F, Sampayo F, Fetal echocardiography. (1993) The results of a 3-year study. Acta Medica Port 6(Suppl 1):I9-13
Sridevi S, Nirmala S (2015) ANFIS based decision support system for prenatal detection of truncus arteriosus congenital heart defect, Appl Soft Comput J, ASOC-3182
Bonnet D (2021) Impacts of prenatal diagnosis of congenital heart diseases on outcomes. Transl Pediatr 10(8):2241–2249. https://doi.org/10.21037/tp-20-267
Huiling W, Bingzheng W, Lai F, Liu P, Lyu G, He S, Dai J Application of artificial intelligence in anatomical structure recognition of standard section of fetal heart, computational and mathematical methods in medicine. Hindawi 2023:5650378. https://doi.org/10.1155/2023/5650378
Luo Y, Zhenkun L, Lu L, Longzhong L, Qinghua H (2023) Deep fusion of human-machine knowledge with attention mechanism for breast cancer diagnosis. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2023.104784
Huang Q, Yang J, Feng X (2020) Automated trading point forecasting based on bicluster mining and fuzzy inference. IEEE Trans Fuzzy Syst 2:259–272
Huang Q, Wang D, Lu Z, Zhuo S, Li J, Liu L, Chang C (2023) A novel image-to-knowledge inference approach for automatically diagnosing tumors. Expert Systems with Applications 229:120450. https://doi.org/10.1016/j.eswa.2023.120450
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kavitha, D., Geetha, S. & Geetha, R. An adaptive neuro fuzzy methodology for the diagnosis of prenatal hypoplastic left heart syndrome from ultrasound images. Multimed Tools Appl 83, 30755–30772 (2024). https://doi.org/10.1007/s11042-023-16682-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-16682-2