A New Technique for Reducing the Segmentation Error of Left Ventricle Contours using Magnetic Resonance Images

Amjad, Khan and Dayang Nurfatimah, Awg Iskandar and Wang, Yin Chai and Lim, Phei Chin (2022) A New Technique for Reducing the Segmentation Error of Left Ventricle Contours using Magnetic Resonance Images. In: 2021 International Conference on Frontiers of Information Technology (FIT), 13-14 December 2021, Islamabad, Pakistan.

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Abstract

The number of Cardiac Magnetic Resonance Images produced for a patient is overwhelming, and this leads to several issues such as labour intensive, time-consuming and detected contours error. The current practice to evaluate the Cardiac Magnetic Resonance Images by the experts is either manual or semi-automatic. Ideally, an automatic evaluation is preferred to assist the cardiac experts in their clinical evaluation. The automatic segmentation for the left ventricle that is the endocardium (Endo) and epicardium (Epi) is currently lacking. The two most usable segmentation model approaches namely Level Set Model (LSM) and Variational LSM (VLSM) are very popular because of their fast iterating of the contours shape objects. Due to the unstructured LV shape and papillary muscles of the left ventricle, both models issue was re-initialisation on detected contours. In this paper presented, the combined Sign-Euclidean distance function takes the distance measurement, from centre to endocardium contour towards Epicontour. The distance measurement function using the distance mapping technique is guided by the curves line using energy function to reduce segmentation error. The experiments were conducted utilizing the Sunnybrook and Pusat Jantung Sarawak (PJS) cardiac datasets. The results shows that the Sign-Euclidean distance function reduces segmentation error between segmented contours, the highest error identified in Endo-contour is; HF-I05 (Endo-14.74); HF-NI-11 (Endo-8.79); P-A004 (Endo-8.04); in Epi-contours; HF-I-08 (Epi-3.08); HF-NI-07 (Epi-2.81); P-AOOI (Epi-3.34). This paper aims to develop a combined Sign-Euclidean distance function that measures among segmented contours and reduces segmentation error against ground-truth contour.

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