Mitral Valve Leaflets segmentation approaches based upon Frangi filter and ISODATA clustering

ABSTRACT

Early detection of chordae tendineae state through medical imaging has a significant impact in reducing the clinical progression of mitral valve disease. This study proposes two methods for tracking these structures. The first method is based on Frangi filter to detect tubular objects while the second one is mainly based on ISODATA clustering approaches. Twenty-eight patients who had previously undergone CT study for mitral valve evaluation were retrospectively enrolled in the study. Pearson correlation coefficients and a linear regression analysis were used to evaluate the correlation between clinical indices measurements for mitral valve segmentation derived from manual segmentation and those obtained through two semi-automated methods. The results showed that MV segmentation based on Frangi filter gave an average Dice index of 0.79 ± 1.52 and a HD of 2.67± 1.14 mm while the ISODATA algorithm indicated an average Dice index of 0.65 ± 0.24 and a HD of 3.26± 0.95 mm. The Pearson correlation coefficients showed a strong correlation between clinical measurements (r= 0.921 for P-A diameter and r=0.987 for MA area, p < 0.01) for mitral valve segmentation derived from manual segmentation and those obtained through Frangi filter method. Similarly, we reported a good correlation between ISDATA and manual segmentation in the measurement of P-A diameter and MA area with respectively r = 0.872 and r = 0.859. The Frangi filter algorithm performs better than the ISODATA algorithm. This suggests that MV segmentation using Frangi filter algorithm gives a performance, which is quite similar to the expert human segmentation.

KEYWORDS:

• Mitral valve
• chordae tendineae
• CT-scan image
• Frangi filter
• ISODATA clustering technique
• segmentation

List of abbreviations

CT-Scan: computed tomography scan

MV: mitral valve

MVCT: mitral valve chordae tendineae

MVR: mitral valve regurgitation

LV: left ventricle

ISODATA: iterative self-organizing data

CLAHE: contrast limited histogram equalization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ramzi Mahmoudi

R. Mahmoudi received his Engineering degree in Computer Sciences from University of Sousse in 2007 and his Master degree (in 2008) and Ph.D. (in 2011) from the University of Marne La Vallée Paris-EST. He finished his post-doctoral studies at the University of California, Irvine in 2012. In addition to teaching, He also supervised many works in close relation with his research.

Narjes Benameur

Narjes Benameur received the Ph.D degree in Biophysics and Medical Imaging from the higher institute of medical technologies of Tunis-Tunisia. She is currently a post-doctoral researcher with the laboratory of Biophysics and Medical Technologies, Tunis, Tunisia. Her current research interests include medical imaging, with a focus on cardiac motion estimation, segmentation and statistical modelling. Since 2015, she started getting interested in tumour hypoxia, and in particular in the use of hyperthermia for cancer treatment.

Hmida Badii

Badii Hmida is a radiologist and an associate professor in Medical Imaging. His expertise areas include general radiology, interventional radiology, cardiac imaging, teleradiology and postgraduate teaching. Dr HMIDA holds university degrees (DIU and DU) in endocascular therapy, interventional radiology oncology, interventional radiology musculoskeletal, cardiovascular imaging, emergency imaging, ORL and cervicofacial imaging, educational technologies in the sciences of health, DU pedagogy in health sciences, clinical research, simulation in health sciences, and telemedicine.

Momahed Hedi Bedoui

Momahed Hedi Bedoui received his Ph.D. degree in biomedical engineering from Lille University, Villeneuve-d’Ascq, France, in 1992. He is currently a Professor of Biophysics with the Faculty of Medicine, Monastir University, Monastir, Tunisia. He is the Director of the ‘Technologie et Imagerie Médicale’ Laboratory with the Faculty of Medicine, Monastir University. He has several published papers in international journals. His main research topics are biophysics, medical imaging processing, embedded system, and codesign HW/SW.