International Conference on Computer and Knowledge Engineering

Home / 15th International Conference on Computer and Knowledge Engineering

Atlas-based segmentation of cardiac chambers in systolic and diastolic phases of echocardiographic images

Authors :

Elham Fathipour¹ Mahdi Saadatmand²

1- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran 2- Associate Professor, Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

Keywords :

Medical atlas،Echocardiographic images،Medical image registration،Image segmentation،Unbiased atlas

Abstract :

During the past few decades, echocardiography has emerged as a low-cost standard imaging modality in cardiology because of its ability to provide non-invasive images of the heart. An echocardiogram, despite its image artifacts, provides detailed information about the structure and function of the heart. To enhance chamber segmentation accuracy in this complex organ, an atlas-based approach is utilized, leveraging prior knowledge for a fast, simple framework. This study proposes segmenting left heart chambers and extracting boundaries using an atlas constructed with a second-order polynomial nonlinear transformation followed by affine transformation, with iterative reference image updates to avoid bias. Registration between the atlas and subject images involves three steps: affine, second-order polynomial, and non-rigid transformations. In order to improve the accuracy in systole phase images,the Radon transform is applied to a small area around the middle border of two chambers. The average Dice coefficient accuracy is 0.902±0.047 for left Ventricle and 0.915±0.026 for the left Atrium in systole, and 0.912±0.031 and 0.881±0.045 in diastole phase, respectively. This method, applied to apical four-chamber views, also can be used in other echocardiographic views.

Papers List

List of archived papers

DTranIDS: A Two-Tiered Intrusion Detection System for RPL-based IoT Networks based on Decision Tree and Transformer Models

Mohammad Fazeli - Mohsen Raji - Mohammad Mahdi Fazeli

Design and Simulation of a Low PDP Full Adder by Combining Majority Function and TGDI Technique in CNTFET Technology

Mahsa Mohammadi

Binary Classification of Capuchin Bird Calls via Spectrogram-Enhanced Frequency-Aware Convolutional Neural Networks

Samad Najjar-Ghabel - Shamim Yousefi - Reza Danandeh Bileh Savar

Soccer Video Event Detection Using Metric Learning

Ali Karimi - Ramin Toosi - Mohammad Ali Akhaee

Probabilistic Short-Term Load Forecasting Using GBDT-Based Sister Forecasts and Ensemble Methods

Hossein Shahinzadeh - Hamed Nafisi - Amirafshin Zamani - Saiedeh Mehrabani-Najafabadi - Arezou Mahmoudi - Farshad Ebrahimi

Autonomous Drone Navigation Using Synchronized Camera and IMU Data with CNN

Reza Javanmard Alitappeh - Narges Hamzeh Mermeti - Fatemeh Barzegar - Fatemeh Ebrahimi - Nima Mahmoudi - Jalal Alipour Langouri

An Advanced Dual Attention-based U-Net Using Breast Ultrasound Data for Image Segmentation

Erfan Akbarnezhad Sany - Niloufar Asghari - Fatemeh Naserizadeh - Seyyed Abed Hosseini

Plant Disease Detection Using Dynamic Knowledge Distillation and Attention Mechanism

Mohammad Ghasemi Arian - Mohammad Hossein Yaghmaee Moghaddam

AVID: A VARIATIONAL INFERENCE DELIBERATION FOR META-LEARNING

Alireza Javaheri - Arsham Gholamzadeh Khoee - Saeed Reza Kheradpisheh - Hadi Farahani - Mohammad Ganjtabesh

Evaluation of Efficient Electrocardiomatrix-based Identification Using Deep Learning Methods

Amirhossein Safari - Narges Mokhtari - Mohsen Hooshmand - Sadegh Sadeghi - Peyman Pahlevani