Textured Image Segmentation Using Deep LawsNet

Abstract

In recent years, image analysis has become crucial across various fields, from medical diagnostics to autonomous vehicles. At the core of image analysis lies segmentation, a critical step that influences the performance of subsequent processes such as object detection and recognition. This study focuses on heart tumor segmentation in CT images using deep learning, particularly convolutional neural networks (CNNs). We utilized the U-Net architecture, renowned for its precision in medical image segmentation, and introduced a modified version called Law-Net, which incorporates Laws filters.Our work is divided into four chapters: an introduction to medical imaging and segmentation techniques, a detailed overview of U-Net architecture, an exploration of Laws filters and the modified U-Net, and an experimental analysis of our model's performance. By applying various segmentation evaluation metrics, we demonstrated the effectiveness of our approach in accurately detecting and segmenting heart tumors.The results indicate that our model, leveraging widely available data and optimized parameters, achieved high performance across all evaluation metrics. This confirms the potential of deep learning in aiding objective medical diagnoses and underscores its capacity to enhance the accuracy and efficiency of tumor detection in CT images. Our deep learning model holds promise for significantly improving the diagnostic process for medical professionals.